Systems and Soft Computing最新文献

A multi-objective game theory model for sustainable profitability in the tourism supply chain: Integrating human resource management and artificial neural networks

Systems and Soft Computing

Pub Date : 2025-03-08 DOI: 10.1016/j.sasc.2025.200217

Amirhossein Torkabadi , Mobina Mousapour Mamoudan , Babek Erdebilli , Amir Aghsami

The tourism industry is a major economic sector worldwide, significantly contributing to job creation and GDP growth. However, the rapid expansion of this industry, along with rising environmental and social concerns, underscores the critical need for sustainable strategies. This paper presents a novel multi-objective game theory model that simultaneously optimizes profitability and sustainability in the tourism supply chain. The key innovation of this study lies in the integration of game theory with an artificial neural network (ANN) to predict customer demand, effectively capturing nonlinear consumer behaviors and enabling more accurate decision-making. The model analyzes the dynamic interactions between tour operators and local service providers, identifying Nash Equilibrium outcomes where no player can improve profitability through unilateral strategy adjustments. Additionally, the study introduces a comprehensive approach to government subsidies, evaluating their effectiveness in enhancing sustainability incentives and overall profitability. A detailed sensitivity analysis is conducted to examine how variations in pricing, sustainability efforts, and subsidy rates influence profit margins. Another distinctive contribution of this research is its emphasis on human resource management, highlighting how employee training, green organizational culture, and financial incentives can improve productivity and support sustainability initiatives. The results demonstrate that collaborative strategies, such as resource sharing and joint sustainability efforts between tour operators and local providers, significantly increase profitability. The findings further indicate that a combination of optimal pricing, maximum sustainability efforts, and full government subsidies yields the highest total profit of 6,395 units. Overall, this research offers strategic guidelines for pricing, human resource development, and subsidy policies, providing a robust framework for achieving both profitability and sustainability in the tourism supply chain.

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引用次数: 0

Tourism supply chain resilience assessment and optimization based on complex networks and genetic algorithms

Systems and Soft Computing

Pub Date : 2025-03-07 DOI: 10.1016/j.sasc.2025.200214

Jie Zheng

Tourism supply chain (TSC) resilience is a measure of the TSC's response to external risks. Currently, intelligent models related to TSC resilience are basically blank. This article is based on the Collaborative Planning Forecasting and Replenishmen (CPFR) model to study the supply chain collaboration mode of smart tourism, providing a train of thought for the research of smart tourism, the purpose is to further improve the accuracy of tourism supply chain toughness assessment, and provide theoretical support for scenic spots to improve their own supply chain toughness. Simultaneously combining machine learning methods to construct a supply chain collaborative prediction model provides a new approach for collaborative prediction in the supply chain. This paper proposes a collaborative model of smart TSC based on CPFR, which not only reflects the operation process of smart TSC, but also incorporates the idea of CPFR to integrate the smart TSC into a system that can operate stably and effectively. Moreover, this paper proposes a resilience evaluation and forecasting algorithm of TSC combining complex network and genetic algorithm with genetic algorithm. In addition, this paper predicts the ability of TSC to cope with external shocks while assessing the resilience of TSC. Finally, according to the experimental research results, the model can converge after 50 iterations, and the prediction error accuracy of the test set is 5.68%, which is improved compared with the existing models The most important influencing factor in the evaluation of tourism supply chain elasticity is the tourist attractions themselves, followed by the economic environment and tourism facilities and services. Under the premise of investment level of 100, the evaluation results of the three are 33.25, 19, 19, respectively. The model proposed in this paper can realize the early forecasting of the TSC, improve the ability of the TSC to cope with risks, and promote the effective improvement of the resilience of the TSC.

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引用次数: 0

Application of CNN-based financial risk identification and management convolutional neural networks in financial risk

Systems and Soft Computing

Pub Date : 2025-03-04 DOI: 10.1016/j.sasc.2025.200215

Zhen Wang

The application of intelligent financial analysis model to the research of enterprise financial risk prediction can improve the adaptability of the model, effectively capture complex patterns and adapt to large-scale data, but there are some problems such as insufficient accuracy and low recall rate. In order to improve the effect of enterprise financial risk management, this research applies convolutional neural network to enterprise financial risk management, and proposes a binary classification prediction model of financial risk dilemma based on one-dimensional convolution and sparse attention mechanism. Then, combined with experimental research, this research verifies that the synergy of multiple modules enables the model proposed in this research to understand and classify the input data more comprehensively and accurately, and then achieve significant improvements in various indicators. Moreover, compared with the comparison model of a single module, it shows superior performance. After training, the accuracy of the model is 75.98 % in the training set and 82.34 % in the test set, which shows the ideal training results, and proves that the model has good generalization ability The model has the best performance in precision, recall and F1, which is due to the comprehensive use of CNN module, LSTM module, encoder module and AR module. After training, the accuracy of the model is 75.98 % in the training set and 82.34 % in the test set, which shows the ideal training results, and proves that the model has good generalization ability. The model has the best performance in precision, recall and F1, which is due to the comprehensive use of CNN module, LSTM module, encoder module and AR module. The experimental results show that the model proposed in this research can realize the accurate classification of binary classification prediction of financial risk dilemma, help enterprises to rationally allocate resources, control the government's unnecessary financial support to enterprises that are on the verge of bankruptcy and have no prospect, and prevent the loss of enterprises' assets.

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引用次数: 0

Forecasting the Bitcoin price using the various Machine Learning: A systematic review in data-driven marketing

Systems and Soft Computing

Pub Date : 2025-02-25 DOI: 10.1016/j.sasc.2025.200209

Payam Boozary, Sogand Sheykhan, Hamed GhorbanTanhaei

The emergence of Bitcoin as a pioneering cryptocurrency has transformed financial markets, garnering widespread interest from academicians, policymakers, and investors. The market's inherent volatility and the rapid integration of public information into price movements continue to present a formidable challenge in accurately forecasting Bitcoin prices despite its potential. The limitations of conventional financial models, which frequently need to consider the distinctive attributes of cryptocurrencies, further exacerbate this challenge. Despite the proliferation of ML in various fields, existing models have not fully harnessed these techniques, performing only marginally better than random guesses due to the unique challenges posed by the high volatility and complex dynamics of cryptocurrency markets. This study introduces a systematic review of ML methods specifically tailored for Bitcoin price prediction, with a focus on evaluating the robustness, accuracy, and appropriateness of advanced ML techniques like Long Short-Term Memory (LSTM) networks. The novelty lies in its comprehensive assessment of these methods in the context of data-driven marketing, aiming to enhance both academic understanding and practical applications in financial technology. The previous studies haven't Machine Learning (ML) has become a formidable instrument that has the potential to improve the accuracy of forecasting; however, there still needs to be more comprehension regarding the most effective ML models in this field. The study's importance is derived from its systematic examination of various machine learning (ML) techniques employed to predict the price of Bitcoin, with a particular emphasis on their integration into data-driven marketing strategies. The results will substantially contribute to both academic research and practical applications, providing valuable insights that can be used to develop more dependable forecasting tools, thereby benefiting investors, marketers, and policymakers.

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引用次数: 0

Optimizing multilevel image segmentation with a modified new Caledonian crow learning algorithm

Systems and Soft Computing

Pub Date : 2025-02-25 DOI: 10.1016/j.sasc.2025.200206

Osama Moh'd Alia

Image segmentation is a fundamental component of image analysis. While numerous algorithms exist for this task, thresholding is one of the most widely used methods. Multilevel thresholding, which involves dividing an image into multiple segments, is computationally intensive due to its need to search for optimal thresholds. This paper presents a solution to this optimization problem by exploring the New Caledonian crow learning algorithm (NCCLA). Inspired by nature, the NCCLA algorithm draws from the behaviors of New Caledonian crows, which use tools from Pandanus trees to obtain food. To improve the algorithm's capacity to discover optimal thresholds while balancing the exploitation and exploration processes, this paper introduces a modification inspired by the pitch adjustment rate portion of the harmony search algorithm. The performance of this modified NCCLA algorithm was evaluated on benchmark images, and a comparative analysis was conducted against other metaheuristic-based algorithms including particle swarm optimization, harmony search, bacterial foraging, and genetic algorithms; the experimental results demonstrate the effectiveness of the proposed algorithm, which was further statistically validated using a t-test.

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引用次数: 0

Design of an intelligent grading system for college English translation based on big data technology

Systems and Soft Computing

Pub Date : 2025-02-19 DOI: 10.1016/j.sasc.2025.200205

Xiaoyan Li , Chengzhou Huang

Background

The Intelligent Grading System (IGS), now in use for college English translation, has the following problems in actual use: the running time is excessive, and the final score result differs significantly from the actual one. Given this scenario, it is essential to substantially increase grading efficiency and result correctness to reduce manual participation in the enhancing grading efficiency.

Objective

This research investigates the use of big data technology in designing an IGS for college English translation. The study focuses on the intersection of literature and English language teaching, aiming to enhance the accuracy and efficiency of grading translation assignments.

Methods

The deep learning methodology is the core approach for developing the intelligent grading system. By leveraging the power of a Hybrid gradient-boosting decision tree with an ensemble Back Propagation Neural Network (HGBDT-EBPNN), the system learns from large volumes of labeled translation data to identify patterns and extract meaningful features that contribute to accurate grading.

Results

The findings of this research contribute to the growing body of knowledge on the use of big data technology and deep learning in the field of translation assessment. The proposed study has provided 98 % of accuracy in the performance metrics.

Conclusion

The IGS offers a promising solution for enhancing the efficiency and objectivity of grading college English translation assignments. It could improve the quality of feedback provided to students as well as streamline the assessment process for instructors.

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引用次数: 0

Application of interactive AI system based on image recognition in rural landscape design

Systems and Soft Computing

Pub Date : 2025-02-19 DOI: 10.1016/j.sasc.2025.200204

Da He

Rural landscape design plays a critical role in improving the quality of rural environment and residents' quality of life. However, the relevant material library is not sufficient and some original styles need to be preserved, making the design work difficult. The research aims to construct an intelligent and interactive rural landscape design system to improve design efficiency and optimize design outcomes. The study uses attention generative adversarial networks to enrich elements in rural landscape design, improving the problem of inaccurate mapping. In addition, a stable diffusion model is introduced to optimize the quality of its landscape mapping. The findings denoted that the intelligent landscape design system designed in the study had an 8-fold reduction in drawing time compared to designer drawings. The generated rural landscape plan was evaluated in detail, including image quality, element diversity, similarity, and referenceability, with a total score of 99.3 points. The overall evaluation score for the overall image performance was 93.9 points, both of which were superior to other intelligent landscape design systems. From this, it can be seen that the research system not only has efficient design efficiency but also meets the requirements for the image quality of the landscape plan drawn and adaptability to the environment. The study proposes a new landscape design tool that contributes to the environmental improvement of rural landscapes.

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引用次数: 0

Neural networks optimization via Gauss–Newton based QR factorization on SARS-CoV-2 variant classification

Systems and Soft Computing

Pub Date : 2025-02-14 DOI: 10.1016/j.sasc.2025.200195

Mohammad Jamhuri , Mohammad Isa Irawan , Imam Mukhlash , Mohammad Iqbal , Ni Nyoman Tri Puspaningsih

Studies on the COVID-19 pandemic continue due to the potential mutation creating new variants. One response to be aware of the situation is by classifying SARS-CoV-2 variants. Neural networks (NNs)-based classifiers showed good accuracies but are known very costly in the learning process. Second-order optimization approaches are alternatives for NNs to work faster instead of the first-order ones. Still, it needs a huge memory usage. Therefore, we propose a new second-order optimization method for NNs, called QR-GN, to efficiently classify SARS-CoV-2 variants. The proposed method is derived from NNs and Gauss–Newton with QR factorization. The goal of this study is to classify SARS-CoV-2 variants given their spike protein sequences efficiently with high accuracy. In this study, the proposed method was demonstrated on a public dataset for the protein SARS-CoV-2. In the demonstrations, the proposed method outperformed other optimization methods in terms of memory usage and run time. Moreover, the proposed method can significantly elevate the accuracy classification for various NNs, such as: single layer perceptron, multilayer perceptron, and convolutional neural networks.

{"title":"Neural networks optimization via Gauss–Newton based QR factorization on SARS-CoV-2 variant classification","authors":"Mohammad Jamhuri , Mohammad Isa Irawan , Imam Mukhlash , Mohammad Iqbal , Ni Nyoman Tri Puspaningsih","doi":"10.1016/j.sasc.2025.200195","DOIUrl":"10.1016/j.sasc.2025.200195","url":null,"abstract":"<div><div>Studies on the COVID-19 pandemic continue due to the potential mutation creating new variants. One response to be aware of the situation is by classifying SARS-CoV-2 variants. Neural networks (NNs)-based classifiers showed good accuracies but are known very costly in the learning process. Second-order optimization approaches are alternatives for NNs to work faster instead of the first-order ones. Still, it needs a huge memory usage. Therefore, we propose a new second-order optimization method for NNs, called <em>QR-GN</em>, to efficiently classify SARS-CoV-2 variants. The proposed method is derived from NNs and Gauss–Newton with QR factorization. The goal of this study is to classify SARS-CoV-2 variants given their spike protein sequences efficiently with high accuracy. In this study, the proposed method was demonstrated on a public dataset for the protein SARS-CoV-2. In the demonstrations, the proposed method outperformed other optimization methods in terms of memory usage and run time. Moreover, the proposed method can significantly elevate the accuracy classification for various NNs, such as: single layer perceptron, multilayer perceptron, and convolutional neural networks.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200195"},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research and application of optimization of physical education training model based on multi-objective differential evolutionary algorithm

Systems and Soft Computing

Pub Date : 2025-02-13 DOI: 10.1016/j.sasc.2025.200200

Man Wu

With the development of computer science, various algorithm models are gradually applied in various fields of life. In order to study the application of the multi-objective differential evolution algorithm in the field of sports transportation. Based on the improvement of multi-objective differential evolution algorithm, this paper proposes the training model of PE education, and compares the prediction results and the actual results. The specific conclusions are as follows: (1) MODE algorithm is better to other algorithms in convergence speed and accuracy; MODE algorithm can not only reach the optimal particle position quickly, but also fluctuate around the best point.(2) AMODE-MPS has great potential for dealing with complex and multiple objectives.(3) There are significant differences between the prediction performance of the proposed algorithm model and the statistical performance, in which the statistical performance is significantly higher than the predicted performance.(4) The proposed model can basically meet the prediction requirements. Although there are some differences between the prediction results and the actual results, this is because the statistical process is affected by the weather, physical condition and other factors. The results show that the PE training model has good results in practice, so this paper can provide reference for the improvement of PE teaching model.

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引用次数: 0

Controllability results for multi-order impulsive neutral fuzzy functional integro-differential equations with finite delay

Systems and Soft Computing

Pub Date : 2025-02-10 DOI: 10.1016/j.sasc.2025.200202

T. Gunasekar , J. Thiravidarani , P. Raghavendran , B.N. Hanumagowda , Jagadish V. Tawade , Farrukh Yuldashev , Manish Gupta , M. Ijaz Khan

This manuscript focuses on examining the controllability of fuzzy mild solutions for nonlocal impulsive neutral functional integro-differential equations of the first and second order, including systems with finite delay. Furthermore, it explores the characteristics of fuzzy set-valued mappings over real variables, emphasizing important features such upper semi-continuity, convexity, normalcy, and compact support. The key conclusions are obtained by applying the Banach fixed-point theorem. The study makes extensive use of fundamental ideas from functional analysis, fuzzy set theory, and the Hausdorff metric. To demonstrate the practical application of the proposed method, a detailed example is provided.

引用次数: 0