Due to technical advancements in this digital era, particularly in the e-commerce sector, the corporate paradigm has changed substantially. A common platform for advertising products is online marketplaces, sometimes ...
详细信息
ISBN:
(数字)9798350354171
ISBN:
(纸本)9798350354188
Due to technical advancements in this digital era, particularly in the e-commerce sector, the corporate paradigm has changed substantially. A common platform for advertising products is online marketplaces, sometimes known as ecommerce platforms. Businesses in the increasingly competitive e-commerce space require a strong marketing plan to boost sales conversion and differentiate between the competitions. Selected features, preprocessing, and training of models all depend on correct sequencing. The proposed approach utilized the SG smoothing filter during the preprocessing phase. Principal component analysis (PCA) is a statistical method that can be employed in feature selection to decrease the dimensionality of a dataset that contains numerous associated variables. Precise control over the qualities is necessary for RNN-RBM training. This approach appears to be far more cutting-edge than the current RBM and RNN algorithms. A significant improvement in accuracy was noted in the results, which reached 96.33%.
Successful operations in industries such as recycling, manufacturing and quality control depend on identifying and classifying different types of glass. Traditional methods, including manual analysis and traditional i...
详细信息
ISBN:
(数字)9798350354171
ISBN:
(纸本)9798350354188
Successful operations in industries such as recycling, manufacturing and quality control depend on identifying and classifying different types of glass. Traditional methods, including manual analysis and traditional image processing methods, often suffer from errors and inconsistencies due to human factors and variations. This paper introduces a new automatic glass type identification system that uses advanced learning techniques, specifically K-Nearest Neighbors (KNN), CNN,RNN. By applying these algorithms, it is aimed to increase the accuracy and performance of the system’s glass type classification. Next, we delve into the design and implementation of machine learning models by discussing data collection techniques, feature extraction technology, and training methods. A comparative analysis of the performance of KNN, RNN, Naive Bayes, Logistic Regression, Random Forest, Feed Forward Network, RNN, ANN, CNN. is performed and their accuracy, performance, and robustness are evaluated in different situations. It shows the most accurate and reliable glass type classification among traditional methods. This study also explores the benefits of using such systems in the manufacturing industry and shows how work efficiency can be increased and the number of errors reduced.
In the field of internet finance, traditional risk warning methods often overlook unstructured data, which mostly contains rich risk information. With the advent of the big data era, Data Mining (DM) algorithms can mi...
ISBN:
(纸本)9798400716478
In the field of internet finance, traditional risk warning methods often overlook unstructured data, which mostly contains rich risk information. With the advent of the big data era, Data Mining (DM) algorithms can mine hidden risk information in data through deep analysis and learning, thereby achieving effective warning of internet financial risks. This article mainly introduces a DM algorithm based on RBF (Radial Basis Function) neuralnetworks (NN) for optimization and improvement. This article first discusses the advantages of RBF NN in risk warning and then analyzes their applications in risk identification, risk assessment, and risk control stages using this algorithm. Subsequently, the calculation formulas for quantifying the risk value and conditional risk value were provided, and a detailed discussion was conducted on the risk warning strategy - setting thresholds, triggering conditions, and measures to be taken after triggering the warning. Finally, a risk warning analysis model for internet finance was designed. Through two sets of simulation experiments, the following conclusions were drawn: compared with traditional methods, the RBF scheme's risk identification accuracy index has an average improvement of about 14.3%, while the risk control loss rate index has an average decrease of about 6.5%. This research achievement can improve the risk management capabilities of internet financial institutions and help predict and reduce potential financial risks, thereby maintaining the stability of the financial market and protecting the interests of investors.
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