Evaluation Computing of Cultural Tourism Resources Potential Based on SVM Intelligent Data Analysis and IoT

Evaluation computing of the cultural tourism resources potential based on SVM intelligent data analysis and IoT is analyzed in this paper. The research highlights are as follows: (1) In order to effectively improve classification performance of symbol data, a symbolic space representation method is defined by deepening the spatial structure relationship between different attribute values and labels of symbol data. (2) Some parallel operators and parameters are adjusted to optimize the performance of the new algorithm. (3) In the security analysis of the actual protocols, the proof method of reduction is often adopted to reduce the security proof of the protocols to some recognized difficult problems. The simulation results prove the effectiveness of the proposed method.

[1]  Kakali Chatterjee,et al.  Cloud security issues and challenges: A survey , 2017, J. Netw. Comput. Appl..

[2]  Alberto Ochoa,et al.  Analyzing Students Reviews of Teacher Performance Using Support Vector Machines by a Proposed Model , 2018 .

[3]  Nasser Ghadiri,et al.  Fuzzy Least Squares Twin Support Vector Machines , 2015, Eng. Appl. Artif. Intell..

[4]  Haoxiang Wang,et al.  Efficient IoT-based sensor BIG Data collection-processing and analysis in smart buildings , 2017, Future Gener. Comput. Syst..

[5]  Zeeshan Ahmed,et al.  IntelliEppi: Intelligent Reaction Monitoring and Holistic Data Management System for the Molecular Biology Lab , 2019 .

[6]  Zoila Esther Morales Tabares,et al.  Intelligent Data Analysis to Calculate the Operational Reliability Coefficient , 2018, IWAIPR.

[7]  Mahmoud Al-Ayyoub,et al.  Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels' reviews , 2017, J. Comput. Sci..

[8]  Abinet Tesfaye Eseye,et al.  Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information , 2018 .

[9]  Hossam Faris,et al.  Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm , 2018, Cognitive Computation.

[10]  Vinod Kumar,et al.  Detection of myocardial infarction in 12 lead ECG using support vector machine , 2018, Appl. Soft Comput..

[11]  Yanjun Shi,et al.  Two-step-hybrid model based on data preprocessing and intelligent optimization algorithms (CS and GWO) for NO2 and SO2 forecasting , 2019, Atmospheric Pollution Research.

[12]  Marcos E. Orchard,et al.  On-line estimation of the aerobic phase length for partial nitrification processes in SBR based on features extraction and SVM classification , 2018 .

[13]  Haoxiang Wang,et al.  Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG , 2018 .

[14]  S. Smys,et al.  Trust-Based Intrusion Detection and Clustering Approach for Wireless Body Area Networks , 2018, Wirel. Pers. Commun..

[15]  Mustafa Zuhaer Nayef Al-Dabagh,et al.  Face Recognition System Based on Kernel Discriminant Analysis, K-Nearest Neighbor and Support Vector Machine , 2018 .

[16]  Zoran Vojinovic,et al.  Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery , 2018 .

[17]  Kai Huang,et al.  Design of intelligent carpooling program based on big data analysis and multi-information perception , 2017, Cluster Computing.

[18]  Yang Wang,et al.  Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. , 2018, Cancer genomics & proteomics.

[19]  S. Smys,et al.  A novel report on architecture, protocols and applications in Internet of Things (IoT) , 2018, 2018 2nd International Conference on Inventive Systems and Control (ICISC).

[20]  ChatterjeeKakali,et al.  Cloud security issues and challenges , 2017 .

[21]  Seyed Amir Naghibi,et al.  A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China , 2018, Bulletin of Engineering Geology and the Environment.

[22]  Han Liu,et al.  Multi-task learning for intelligent data processing in granular computing context , 2018 .

[23]  Jie Yang,et al.  Kernelized support vector machine with deep learning: An efficient approach for extreme multiclass dataset , 2017, Pattern Recognit. Lett..

[24]  Sung Wook Baik,et al.  Secure Surveillance Framework for IoT Systems Using Probabilistic Image Encryption , 2018, IEEE Transactions on Industrial Informatics.

[25]  Abien Fred Agarap.  A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data , 2017, ICMLC.