Intelligent Data Analysis - Volume 28, issue 2

Authors: Karthick, M. | Satheesh Kumar, S. | Vivek, D. | Viswanathan, A.

Article Type: Research Article

Abstract: Reaching the maximum number of customers at the right time to increase sales and profitability of the business is the primary goal of Electronic Commerce (E-Commerce). However, owing to the low-influenced product, the profitability of e-commerce has been drastically affected in recent years. Therefore, this work proposes an Empirical Probability assigned Satty’s method integrated Multicriteria Decision-Making model (EP-Satty-MCDM)-based business decision-making model for improving the sales of low-influenced products by advertising with celebrities. Primarily, the authenticated user securely downloaded the encrypted data using Armstrong number private key generated-Trident Curve-Cryptography (Arm-TCC) in the web application. After that, the data is cleansed and …the attributes of review and behavior data are extracted. Then, by utilizing the Interval-valued Atanassov intuitionistic fuzzy-based Mann-Whitney U test (IAF-MWU), the correlation between the review and behavior data is evaluated. The correlated features under each user are mapped under the product, and semantic ontology is constructed, where the data is again mapped with the product’s subsections. Afterward, the domains are extracted. Thereafter, to identify whether the product is high-influenced or low-influenced, the obtained ratings from ontology and extracted domains are inputted into the Boosting Regression Tree-Recurrent Neural Network (BRT-RNN). Then, for the decision-making, the positively forecasted celebrities with their garment and low-influenced products are given as the input to EP-Satty-MCDM. The experimental outcomes exhibited that the proposed technique withstands maximum accuracy when contrasted with the existing methodologies. Show more

Keywords: Ontology, boosting regression tree-recurrent neural network (BRT-RNN), empirical probability assigned Satty’s method integrated Multicriteria decision making model (EP-Satty-MCDM), semantic domain ontology, decision-making model

DOI: 10.3233/IDA-227446

Citation: Intelligent Data Analysis, vol. 28, no. 2, pp. 573-590, 2024

Authors: Chen, Qiulian | Chen, Yan

Article Type: Research Article

Abstract: Utilization of residue is a challenge in engineering practice, because unreasonable cutting causes excess materials wasted and increases the production cost. This work considers the residual two-dimensional cutting stock problem with usable leftover in which unused parts of cutting patterns can be used for future orders. We propose an algorithm that combines the iterative sequential value correction heuristic with the beam search heuristic, considering both the accumulation and the reusability of leftovers to reduce the material consumption. Cutting plans are constructed iteratively and the best one are chosen as the solution. Cutting patterns in the cutting plan are generated sequentially …by recursive techniques, and potentially usable leftover are accumulated by beam search heuristic. Item values are corrected after each pattern to diversify cutting plans. Three sets of simulations under different number of periods, over medium and large instances from the literature, are used to demonstrate the effectiveness of the heuristics. Computational results show that the algorithm provides better solutions, which can save a considerable amount of plate in a long-term production period. The utilization of wastages can save a considerable amount of stock plate and contract the production cost of enterprises in the long-term production period. Show more

Keywords: Cutting stock problem, iterative sequential value correction, beam search, usable leftover, residual sub-plate, Cutting patterns, Heuristics

DOI: 10.3233/IDA-227447

Citation: Intelligent Data Analysis, vol. 28, no. 2, pp. 591-611, 2024

Authors: Gao, Chunjuan

Article Type: Research Article

Abstract: With the continuous progress of science and the development of society, there are also problems of resource shortage and environmental pollution. People are eager to change this situation. How to achieve sustainable development has become an important problem to be solved. The development of society is inseparable from the supply chain, but the traditional supply chain cannot meet the requirements of sustainable development. The research manifests that the Closed-Loop supply chain (CLSC) can protect the environment while economic development, and achieve harmonious coexistence between man and nature. CLSC is studied initially, and the connotation and characteristics of CLSC are introduced. …A management optimization of CLSC based on deep learning technology is proposed. Meanwhile, the genetic simulated annealing algorithm is used for simulation, and the obtained solution is the optimal solution. And this algorithm has good convergence, which provides new ideas for the future development of Chinese enterprises. Show more

Keywords: Deep learning technology, closed-loop supply chain, genetic algorithm, simulated annealing algorithm

DOI: 10.3233/IDA-227449

Citation: Intelligent Data Analysis, vol. 28, no. 2, pp. 613-627, 2024