Systems and Soft Computing最新文献

{"title":"Carbon Emission Modeling of Excavation and Non-excavation Techniques in Overall Repair of Drainage Pipelines","authors":"Xiao Yu ,&nbsp;Xiaodong Hu ,&nbsp;Wen Xie ,&nbsp;Aichen Pan ,&nbsp;Xinke Li ,&nbsp;Huijuan Wang","doi":"10.1016/j.sasc.2025.200213","DOIUrl":"10.1016/j.sasc.2025.200213","url":null,"abstract":"<div><div>Due to the rising levels of CO2 emissions in China, the calculation of carbon emissions and the evaluation of the environmental advantages associated with pipeline maintenance have become significant concerns. This research determined the total carbon emissions formula by breaking down the process of repair into three distinct stages: the production of the material, transportation of the material, and installation of the material. With the use of this calculation, the total carbon emissions of two different pipeline repair methods that did not include excavation and excavation were compared. When compared to the excavation method and the FIPP technique, the CIPP approach produced the greatest total carbon emissions for a pipe with a diameter of DN400. The CIPP technique produced 2.6 times the amount of carbon emissions as the excavation technique. This work offers a complete carbon emission model for both non-extraction methods and excavation in the general drainage pipeline maintenance. Applied to a case study of a drainage pipeline repair project in an urban location, the non-extraction methodology employing trenchless cured-in-place pipes (CIPPs) lowers carbon emissions by 87% relative to conventional excavation methods. The non-extraction method's carbon emissions, specifically, are 12.5 kilogram CO2e/m; the excavation method's emissions are 102.1 kg CO2e/m. The non-extraction method also lowers water use by 90% and energy use by 75%. The findings of this research offer infrastructure managers and legislators important new perspectives to guide their decisions on drainage pipe repair techniques with lowest environmental effect. This study can help pipeline repair firms are provided with a scientific method for measuring carbon emissions and evaluating environmental benefits, which offers substantial support for the companies' efforts to establish themselves as sustainable.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200213"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning based personalized English listening learning path recommendation algorithm","authors":"Hua Jiang","doi":"10.1016/j.sasc.2025.200210","DOIUrl":"10.1016/j.sasc.2025.200210","url":null,"abstract":"<div><div>A crucial aspect of learning a language and listening is competency in English. A information on users' vocabulary complexity, pronunciation, proficiency level,speed reading, topic relevance, objective in learning, data performance and preferences, To evaluate the DL -based personalized learning path recommendation algorithm for English listening instruction can make customized learning path recommendations. In order to enable individualized English listening instruction, this study presents the CNN-PR algorithm, which is based on CNN. The CNN-PR system uses deep learning and data analytics to deliver personalized listening recommendations based on every learner vocabulary complexity, topic relevance,skill level, and reading speed. We assess the algorithm's efficacy using a battery of tests and analyses, considering variables such as adaptability, learner satisfaction, and recommendation rating. The algorithm's capacity to select varied and pertinent listening resources improves student engagement and comprehension, as demonstrated by the results. On the other hand, we recognize difficulties such as algorithmic biases and the need for constant improvement. In the end, the CNN-PR algorithm shows promise as an adaptive learning strategy in language learning, advancing the development of tailored and successful language learning encounters. We used eight high-performance iterations and chose the best four. When compared to other current methods, the suggested algorithm performs under these features with predicted model accuracy, precision, and recall levels: vocabulary complexity accuracy of 97.32 %, proficiency level accuracy of 92.72 %, topic relevance accuracy of 91.62 %, speed reading accuracy of 95.34 %, and the highest CNN-PR accuracy of 97.32 % overall. The experiment's findings show that the study in this paper can, to some extent, recommend the most effective learning paths for the intended users, enhance the accuracy of the suggested resources, and enhance the users' learning experience and quality.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200210"},"PeriodicalIF":0.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Securing the economic management and service infrastructure of banks via the use of artificial intelligence (MO-ILSTM)","authors":"Xintong Wu","doi":"10.1016/j.sasc.2025.200227","DOIUrl":"10.1016/j.sasc.2025.200227","url":null,"abstract":"<div><div>The banking industry has been a key player in economic growth, but the development of economic management and service infrastructures has not significantly reduced the current financial crisis. In service infrastructure or economic management, the challenge of making judgments and processing data inefficiently in unpredictable markets is the drawback of the existing approach. Technology-related constraints, such as scalability and connectivity issues, can hinder the application's functionality and ability to adapt to changing market conditions. Scalability and connectivity constraints can impact applications related to online banking, digital transactions, and financial data processing. This study explores the use of Mothfly Optimized Improved Long Short-Term Memory (MO-ILSTM) as a data classification technique to improve data sharing and processing effectiveness. The proposed approach overcomes the above mentioned constraints. The capacity of LSTM to identify long-range relationships in sequential data is restricted. By boosting data processing and decision-making in service infrastructure or economic management amid market volatility, MO-ILSTM aims to increase long-range dependency capture. The ILSTM approach is extended from binary data classification to various classifications, addressing the inability of economic management or service infrastructure to efficiently handle complex data processing needs and ensure prompt decision-making. The proposed research is to better predict risk, process data more efficiently, integrate economic services into the banking sector, and improve economic management and service infrastructure to lessen the effects of the financial crisis. Tests show that the ILSTM-based economic management and service infrastructure can decrease economic threat by 18 %, increase service quality by 32 %, and increase the degree of integrated economic service by 45 %. The platform can also effectively forecast financial risks, with a prediction accuracy of 75.6 % due to information exchange and interaction. Thus, the ILSTM algorithm can significantly reduce economic risks and enhance the effectiveness of economic management and service infrastructure.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200227"},"PeriodicalIF":0.0,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738625","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}

{"title":"Aggregated approach for interstitial lung diseases classification using attention based CNN and radial basis function neural network","authors":"S. Kumarganesh ,&nbsp;K.V.M. Shree ,&nbsp;P. Rishabavarthani ,&nbsp;C. Ganesh ,&nbsp;S. Anthoniraj ,&nbsp;B. Thiyaneswaran ,&nbsp;Lam Dang ,&nbsp;K. Martin Sagayam ,&nbsp;Linh Dinh ,&nbsp;Hien Dang","doi":"10.1016/j.sasc.2025.200228","DOIUrl":"10.1016/j.sasc.2025.200228","url":null,"abstract":"<div><div>The medical field has significantly advanced with advances in technology, with a focus on biomedical devices and early diagnosis. Image processing techniques and artificial intelligence are used to analyze the lung anatomy and ensure an accurate diagnosis of interstitial lung diseases. This study proposes an automated approach for identifying Interstitial Lung Diseases (ILD) using biomedical images. Computed Tomography (CT) biomedical images were used for analysis. This CT image was analyzed using both radiomic and deep learning features for efficient identification of ILD at an early stage. Here, radiomic features were extracted using gray-level properties and reduced using Particle Swarm optimization with inverse maximization of accuracy and precision as objective functions. The reduced features were then trained and tested using a radial basis function neural network (RBFNN). In parallel, an attention-based convolutional neural network was used to perform deep learning-based ILD classification using gray and local pattern images. Finally, both model outputs were aggregated for the final prediction by evaluating accuracy, precision, and F1-score. The proposed approach outperformed the ensemble approach for ILD classification by increasing its accuracy to 5 % for final prediction.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200228"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746619","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}

{"title":"Image interpretation and generation method integrating block sentinels and AAM in intelligent art design","authors":"Huisan Wang","doi":"10.1016/j.sasc.2025.200231","DOIUrl":"10.1016/j.sasc.2025.200231","url":null,"abstract":"<div><div>In intelligent art and design, image interpretation generation plays a pivotal role in enabling designers to explore and implement creativity in accordance with detailed image descriptions. To achieve more significant results in image interpretation generation, this study innovatively transforms the image interpretation generation problem into a sequence-to-sequence problem. The proposed model is an enhancement of the attention mechanism-based encoding and decoding image interpretation generation model. It is achieved by integrating the block sentinel mechanism and the adaptive attention mechanism. The results showed that the proposed model achieved scores of 19.48 %, 132.52 %, 40.74 %, and 13.47 % in Meteor, Cider, Rouge_L, and Bleu4, which were significantly better than the other comparative models. Meanwhile, the running time of the model in simple and complex scenarios was only 0.38 s and 0.45 s, while the running time of the Up-Down model reached 1.74 s and 3.28 s, significantly higher than the research model. This finding suggests that the image interpretation generation model based on block sentinels and an adaptive attention mechanism can achieve satisfactory image interpretation generation results in various scenarios. The model has been shown to generate image interpretations that are both smoother and more coherent, and it has been demonstrated to possess a higher operational efficiency. This suggests that the model can serve as an effective image interpretation tool for the field of intelligent art and design.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200231"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800105","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}

{"title":"Controllability of Intuitionistic Fuzzy Neutral Integro-Differential Equations with Nonlocal Conditions","authors":"T. Gunasekar ,&nbsp;K. Nithyanandhan ,&nbsp;P. Raghavendran ,&nbsp;B. N Hanumagowda ,&nbsp;Jagadish V Tawade ,&nbsp;Nashwan Adnan OTHMAN ,&nbsp;Manish Gupta ,&nbsp;M. Ijaz Khan","doi":"10.1016/j.sasc.2025.200229","DOIUrl":"10.1016/j.sasc.2025.200229","url":null,"abstract":"<div><div>This paper investigates the controllability of nonlocal intuitionistic fuzzy neutral integro-differential equations using intuitionistic fuzzy semigroups and the contraction mapping principle. By formulating a rigorous theoretical framework, we derive sufficient conditions for ensuring controllability of these systems under nonlocal constraints. The study introduces a novel approach to handling uncertainties inherent in fuzzy systems, demonstrating that intuitionistic fuzzy control functions can effectively manage these complexities. Furthermore, the results provide a foundation for addressing significant challenges in controlling systems with nonlocal features, offering new perspectives for both theoretical advancements and practical implementations. This work paves the way for future research in applying intuitionistic fuzzy control to diverse scientific and engineering problems.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200229"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777448","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}

{"title":"Blended teaching of university mathematics courses based on Online Merge Offline model","authors":"Yuping Zhang,&nbsp;Changzhou Dong","doi":"10.1016/j.sasc.2025.200222","DOIUrl":"10.1016/j.sasc.2025.200222","url":null,"abstract":"<div><div>In order to study the impact of Online Merge Offline (OMO) hybrid teaching model on college mathematics courses, the article constructs a new hybrid teaching model based on the OMO model and evaluates it using a modified oriented evaluation model based on an improved multi-party weighting index algorithm (Context Evaluation-Input Evaluation-Process Evaluation-Product Evaluation, CIPP), to evaluate it. The results show that the entropy method is better in the improved CIPP evaluation model, and the correct rate of the entropy method is 93.25 %. The improved correlation algorithm takes less time and is faster, with the average time of the improved correlation algorithm being 100ms and the lowest time of the original correlation algorithm being 300ms. The hybrid teaching mode is more excellent than the traditional teaching mode, and the rate of student achievement in the hybrid teaching mode is 49.9 higher than the rate of student achievement in the traditional teaching mode.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200222"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777542","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}

{"title":"Remote sensing image fusion based on real time image smoothing and image similarity","authors":"Yanfang Hou ,&nbsp;Kaixuan Guo ,&nbsp;Xueyan Bi","doi":"10.1016/j.sasc.2025.200226","DOIUrl":"10.1016/j.sasc.2025.200226","url":null,"abstract":"<div><div>Real-time image smoothing and image similarity of remote sensing image techniques can help people to obtain image information more accurately in the field of remote sensing. To improve the efficiency of information analysis, image fusion techniques are required. In this study, image preprocessing algorithm is used to smooth remote sensing images for the fusion problem between different remote sensing images. Subsequently, a hybrid algorithm model based on non-negative matrix factorization and block term decomposition is used to fuse remote sensing images. The outcomes indicated that the image preprocessing algorithm preprocessed image had better smoothness and performed better in outdoor scenes, with peak signal-to-noise ratio of 28.26 dB and average structural similarity of 0.91. The remote sensing image of urban landscape scenes fused by the hybrid algorithm model, not only had complete spectral information and feature parameters, but also high clarity. The root mean squared error index of the hybrid remote sensing image was 0.0121, the correlation coefficients was 0.9905, the spectral angle mapping was 0.0198, and the running time was 25s. It can be concluded that by preprocessing remote sensing images and then fusing them, not only can information-rich images be obtained quickly, but also the efficiency of image analysis can be greatly improved. The research not only provides a new method for improving the quality and fusion of remote sensing images, but also offers a new technology for denoising remote sensing images.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200226"},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777449","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}

{"title":"Application of K-means Supported by Clustered Systems in Big Data Association Rule Mining","authors":"Lihua Liu","doi":"10.1016/j.sasc.2025.200211","DOIUrl":"10.1016/j.sasc.2025.200211","url":null,"abstract":"<div><div>Association rule mining plays an important role in the field of data mining, which is used to discover hidden relationships. However, as data volumes increase, traditional association rule mining methods are constrained to single-machine computing when processing large-scale data. These methods are unable to leverage the advantages of modern distributed computing frameworks, resulting in more significant performance bottlenecks when processing large-scale datasets. Therefore, research on how to combine distributed computing technology with association rule mining has become the key to improving efficiency and scalability. To this end, the study introduced a parallel frequent itemset mining technique, FiDoop DP, which used the MapReduce programming paradigm for data partitioning on Hadoop clusters and integrates an improved k-means++ algorithm for data preprocessing to provide better data processing results. The findings indicated that the enhanced k-means++ clustering method achieved a Davies-Bouldin index of 0.642 for performance validation, while its Calinski-Harabasz score reached 5186. The improved k-means++ clustering technique showed advantageous clustering results, while the data partitioning method based on frequent item set parallel mining shown a notable performance advantage. With 60 seed points, the execution time for the frequent item set parallel mining technique was just 683 seconds, the mining duration was only 402 seconds, and the shuffling expenditure amounted to 2280GB. This indicates that the FiDoop DP method proposed by the study has significant importance in modern cluster environments. By combining the distributed computing capabilities of Hadoop clusters with the improved k-means++ clustering algorithm, this method effectively solves the scalability problem in processing large datasets and significantly improves the efficiency of clustering analysis and frequent itemset mining.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200211"},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777447","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}

{"title":"A two-way neural network music separation method for music intelligent classroom","authors":"Yu Yu ,&nbsp;Wei Li ,&nbsp;Li Zhou","doi":"10.1016/j.sasc.2025.200208","DOIUrl":"10.1016/j.sasc.2025.200208","url":null,"abstract":"<div><div>With the promotion of technology for educational reform and innovation, how to broaden the teaching space through technology and create a good classroom atmosphere in the music-smart classroom has become a hot topic for educators to explore. The study discusses music separation techniques based on those commonly used in the intelligent classroom. To address the problem of using the sample timing information in the training process, the study uses LSTM networks instead of traditional recurrent neural networks. It constructs a DS_BRNN algorithm for the separation of accompaniment and song of mixed music. A discriminative training objective function is introduced to train the real part separately from the imaginary part, aiming to extend the separation target from the real domain amplitude spectrum to the complex domain amplitude spectrum. The innovation of this research lies in using the single-channel music separation method to improve the teaching effect of music intelligent classrooms. The results on accompaniment separation performance showed that the DS-BRNN algorithm was 0.161 dB lower than the DNN music separation model in GSAR values but improved by about 2.5–4.3 dB in GSIR and GSDR values. Moreover, it also had a similar performance in separating human voices, while the GSIR value of HPSS was only about 3 dB higher than that of DS-BRNN. The proposed improved algorithm has better comprehensive performance than other traditional separation models in music separation. The primary contribution is to provide technical support for the intelligentization of music classrooms and to establish a theoretical basis and potential applications for the creation of teaching situations that utilize music separation in intelligent music classrooms.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200208"},"PeriodicalIF":0.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685572","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}