Titlebook: Data Engineering and Intelligent Computing; Proceedings of 5th I Vikrant Bhateja,Lai Khin Wee,T. M. Rajesh Conference proceedings 2022 The

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書目名稱Data Engineering and Intelligent Computing影響因子(影響力)




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書目名稱Data Engineering and Intelligent Computing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Data Engineering and Intelligent Computing被引頻次




書目名稱Data Engineering and Intelligent Computing被引頻次學(xué)科排名




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書目名稱Data Engineering and Intelligent Computing讀者反饋學(xué)科排名

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Forehead-Lift

Application-Oriented Content Quality Analysis of Data Using Python,sary to find out the potential consumers or clients so that marketing will be done in an effective manner with addition to that the business makers can gain loyalty of consumers which will be beneficial for them in various perspectives. So, the quality of data is extremely important for business mak

Incumbent

An Improved Similarity Measure Based on Collaborative Filtering for Sparsity Problem in Recommenderlts for the people. Recommender systems (RSs) play a significant role in generating accurate recommendations. Among the different types of RS, collaborative filtering-based (CF) RS is the most used due to its ability to produce recommendations that can fit the user’s varying preferences over time. C

委托

Detection and Classification of Bird Pest Using Spectrogram, Physical Imagery, and Convolutional Nesing physical and acoustic spectrogram imagery and optimized convolutional neural network classifiers. Two CNN models are designed to automatically and hierarchically learn spatially image features using 5 convolution layers of different filters followed by max polling 3 and fully connected 10 NN la

成份

Scatter Index: An Alternative Measure of Dispersion Based on Relative Frequency of Occurrence of ObCV) is a widely used as a standardized measure of dispersion. However, CV is not advisable in applications where mean of dataset is close to zero. In this paper, we have derived an alternate measure of dispersion called . which is based on relative frequency of observations. The novelty of the study

成份

Heart Failure Survival Prediction using Various Machine Learning Approaches, 32% of those deaths occurring globally. Heart attack and stroke accounted for 85% of these deaths. We used chi2 distributor, quantile transformer, polynomial feature, and XGboosting as machine learning approaches in this paper. In addition, the suggested model is used in a variety of machine learni

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