Multi independent latent component extension of naive Bayes classifier

Alizadeh, Sasan H.; Hediehloo, Alireza; Harzevili, Nima Shiri

doi:10.1016/j.knosys.2020.106646

Cited by 23 publications

(17 citation statements)

References 41 publications

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“…It is a good research tool that can manage the complications coming from simulated environments since it is a fully configurable tool that allows the expansion and formulation of rules in every software stack component. The proposed Averaged One-Dependence Estimators (AODE) and SELECT Applicable Only To Parallel Server (ASA) compare with the Beyond fifth Generation (B5G), Fully Automated Unmanned Aerial Vehicles (FAUAV) [ 2 ], Maximum Correlation Criterion, And Minimum Dependence Criterion (MCCMDC) [ 40 ], Multi Independent Latent Component Naive Bayes Classifier (MILC-NB) [ 3 ] and Correlation-Augmented Naïve Bayes (CAN) [ 28 ] Algorithm. The analyze results are then shown in Table 1 and Figure 4 .…”

Section: Results Comparison Discussion With Data Modulesmentioning

confidence: 99%

“…These techniques use (mul), (pow), (pivot), (mod), and (ASCII) as coherent activities for data. In the cloud, private keys are created and sent to the users [ 3 ]. It retains the service in the company’s Key Management ( K m ).…”

Section: Proposed Smart City Data Acquisitionmentioning

confidence: 99%

“…The sensor-based data has advanced, and this controls how cities are formed. Hybrid data obfuscation is used to manage the method of classifying the data for the blockchain facilities [ 3 ]. The security objective for data management and data formation is made for security purposes by data obfuscation [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation

Thirumalaisamy

Basheer

Shitharth

et al. 2022

Sensors

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There can be many inherent issues in the process of managing cloud infrastructure and the platform of the cloud. The platform of the cloud manages cloud software and legality issues in making contracts. The platform also handles the process of managing cloud software services and legal contract-based segmentation. In this paper, we tackle these issues directly with some feasible solutions. For these constraints, the Averaged One-Dependence Estimators (AODE) classifier and the SELECT Applicable Only to Parallel Server (SELECT-APSL ASA) method are proposed to separate the data related to the place. ASA is made up of the AODE and SELECT Applicable Only to Parallel Server. The AODE classifier is used to separate the data from smart city data based on the hybrid data obfuscation technique. The data from the hybrid data obfuscation technique manages 50% of the raw data, and 50% of hospital data is masked using the proposed transmission. The analysis of energy consumption before the cryptosystem shows the total packet delivered by about 71.66% compared with existing algorithms. The analysis of energy consumption after cryptosystem assumption shows 47.34% consumption, compared to existing state-of-the-art algorithms. The average energy consumption before data obfuscation decreased by 2.47%, and the average energy consumption after data obfuscation was reduced by 9.90%. The analysis of the makespan time before data obfuscation decreased by 33.71%. Compared to existing state-of-the-art algorithms, the study of makespan time after data obfuscation decreased by 1.3%. These impressive results show the strength of our methodology.

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Section: Results Comparison Discussion With Data Modulesmentioning

confidence: 99%

Section: Proposed Smart City Data Acquisitionmentioning

confidence: 99%

See 1 more Smart Citation

Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation

Thirumalaisamy

Basheer

Shitharth

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Based on the given training set, it learns the joint probability distribution from input to output under the assumption that the feature conditions are independent. Then, the output was calculated with the maximum probability based on the learned model [29].…”

Section: Model Constructionmentioning

confidence: 99%

Feature Wavelength Selection Based on the Combination of Image and Spectrum for Aflatoxin B1 Concentration Classification in Single Maize Kernels

2022

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Aflatoxin B1 (AFB1) is a very strong carcinogen, maize kernels are easily infected by this toxin during storage. Rapid and accurate identification of AFB1 is of great significance to ensure food safety. In this study, a novel method for classification of AFB1 in single maize kernels was developed. Four groups of maize kernel samples with different AFB1 concentrations (10, 20, 50, and 100 ppb) were prepared by artificial inoculation of toxin. In addition, one group of maize kernel samples without AFB1 were prepared as control, each group with 70 samples. The visible and short wave near-infrared (Vis-SWNIR) region (500–1000 nm) and long wave near-infrared (LWNIR) region (1000–2000 nm) hyperspectral images of all samples were obtained respectively, and the hyperspectral images in 500–2000 nm range was obtained after spectral pretreatment and fusion. Kennard-Stone algorithm was used to divide the samples into calibration set or prediction set. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to roughly select the characteristic wavelengths of the calibration set samples, and 25 and 26 effective wavelengths were obtained respectively. Based on the roughly selected wavelengths, a method of fine selection of the characteristic wavelengths was proposed by using the gray-value difference of image (GDI), and a few number of characteristic wavelengths were further selected. Under the LDA classification model, 10 characteristic wavelengths were selected to test the prediction set and the independent verification samples, and the ideal result were obtained with an accuracy of 94.46% and 91.11%, respectively. This study provides a new approach for AFB1 concentration classification of single maize kernels.

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“…e commonly used plain Bayesian (NB) [32], BP neural network [33], LSTM neural network [34], and DE-CNN were used to compare and analyse the test dataset respectively, as shown in Figure 10.…”

Section: Video Classification Performance Of Different Algorithmsmentioning

confidence: 99%

Dance Art Scene Classification Based on Convolutional Neural Networks

2022

Scientific Programming

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Digital multimedia resources have become an important part of people’s daily cultural life. Automatic scene classification of a large number of dance art videos is the basis for scene semantic based video content retrieval. In order to improve the accuracy of scene classification, the videos are identified using a deep convolutional neural network based on differential evolution for dance art videos. First, the Canny operator is used in YCbCr colour space to detect the human silhouette in the key frames of the video. Then, the AdaBoost algorithm based on cascade structure is used to implement human target tracking and labelling, and the construction and updating of weak classifiers are analysed. Next, a differential evolution algorithm is used to optimise the structural parameters of the convolutional neural network, and an adaptive strategy is adopted for the scaling factor of the differential evolution algorithm to improve the optimisation solution accuracy. Finally, the improved deep convolutional neural network is used to train the classification of the labelled videos in order to obtain stable scene classification results. The experimental results show that by reasonably setting the crossover rate of differential evolution and the convolutional kernel size of the convolutional neural network, high scene classification performance can be obtained. The high accuracy and low root-mean-square error validate the applicability of the proposed method in dance art scene classification.

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Multi independent latent component extension of naive Bayes classifier

Cited by 23 publications

References 41 publications

Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation

Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation

Feature Wavelength Selection Based on the Combination of Image and Spectrum for Aflatoxin B1 Concentration Classification in Single Maize Kernels

Dance Art Scene Classification Based on Convolutional Neural Networks

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