Systems and Soft Computing最新文献

Application of an intelligent English text classification model with improved KNN algorithm in the context of big data in libraries

Systems and Soft Computing

Pub Date : 2025-01-17 DOI: 10.1016/j.sasc.2025.200186

Qinwen Xu

In the era of big data, libraries manage huge electronic text resources, of which English text resources are particularly critical for academic research, student learning, and professional knowledge acquisition. This paper aims to improve the K-nearest neighbor algorithm and design an intelligent classification model to improve the efficiency and quality of library services. An improved method based on in-class K-means clustering and class mean distance is used to characterize and extract text information with a vector space model. The results showed that the improved K-nearest neighbor algorithm achieved significant improvement in the precision, recall, and F1 values, reaching 90.50 %, 89.95 %, and 89.37 %, respectively. The classification time was significantly reduced to 1034.57 s. In addition, the improved algorithm had a classification accuracy of 94 %, surpassing other popular text classification algorithms. The research successfully realizes the efficient classification of text. The research results not only improve the classification efficiency of library English text resources but also provide strong support for readers to quickly obtain the required information, which has important application value and wide application prospects.

{"title":"Application of an intelligent English text classification model with improved KNN algorithm in the context of big data in libraries","authors":"Qinwen Xu","doi":"10.1016/j.sasc.2025.200186","DOIUrl":"10.1016/j.sasc.2025.200186","url":null,"abstract":"<div><div>In the era of big data, libraries manage huge electronic text resources, of which English text resources are particularly critical for academic research, student learning, and professional knowledge acquisition. This paper aims to improve the K-nearest neighbor algorithm and design an intelligent classification model to improve the efficiency and quality of library services. An improved method based on in-class K-means clustering and class mean distance is used to characterize and extract text information with a vector space model. The results showed that the improved K-nearest neighbor algorithm achieved significant improvement in the precision, recall, and F1 values, reaching 90.50 %, 89.95 %, and 89.37 %, respectively. The classification time was significantly reduced to 1034.57 s. In addition, the improved algorithm had a classification accuracy of 94 %, surpassing other popular text classification algorithms. The research successfully realizes the efficient classification of text. The research results not only improve the classification efficiency of library English text resources but also provide strong support for readers to quickly obtain the required information, which has important application value and wide application prospects.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200186"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148527","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

Analyzing the quality evaluation of college English teaching based on probabilistic linguistic multiple-attribute group decision-making

Systems and Soft Computing

Pub Date : 2025-01-17 DOI: 10.1016/j.sasc.2025.200191

Ruoxi Hu , Qingmao Wang

The evaluation of college English teaching quality aims to comprehensively assess the achievement of teaching objectives and effectiveness through the analysis and feedback on teachers' teaching abilities, course design, and students' learning outcomes. The evaluation combines both quantitative and qualitative methods, focusing not only on the scientific and practical aspects of teaching content but also on the improvement of students' language proficiency and overall development. A scientific evaluation system encourages teachers to refine their teaching methods, enhances teaching efficiency, and provides data support for curriculum optimization, thereby continuously improving the quality of college English teaching to meet students' academic and career development needs. The quality evaluation of college English teaching is multiple-attribute group decision-making (MAGDM). To address this, combined TODIM (Logarithmic TODIM and Exponential TODIM) and PROMETHEE approaches are utilized to propose a MAGDM framework. Considering the need to capture fuzzy information during the quality evaluation process, probabilistic linguistic term sets (PLTSs) are employed. In this study, we construct the probabilistic linguistic combined TODIM-PROMETHEE (PL-Com-TODIM-PROMETHEE) approach to tackle MAGDM under PLTSs. To determine the weight values within the PLTSs framework, we employ the MEREC approach. Finally, a numerical example is presented to validate the effectiveness of the PL-Com-TODIM-PROMETHEE approach for quality evaluation of college English teaching. Through this approach, the study contributes to the advancement of quality evaluation methodologies by integrating combined TODIM and PROMETHEE within the PLTSs framework. It addresses the challenges posed by fuzzy information and provides a practical and effective approach for decision-making in the context of quality evaluation of college English teaching.

{"title":"Analyzing the quality evaluation of college English teaching based on probabilistic linguistic multiple-attribute group decision-making","authors":"Ruoxi Hu , Qingmao Wang","doi":"10.1016/j.sasc.2025.200191","DOIUrl":"10.1016/j.sasc.2025.200191","url":null,"abstract":"<div><div>The evaluation of college English teaching quality aims to comprehensively assess the achievement of teaching objectives and effectiveness through the analysis and feedback on teachers' teaching abilities, course design, and students' learning outcomes. The evaluation combines both quantitative and qualitative methods, focusing not only on the scientific and practical aspects of teaching content but also on the improvement of students' language proficiency and overall development. A scientific evaluation system encourages teachers to refine their teaching methods, enhances teaching efficiency, and provides data support for curriculum optimization, thereby continuously improving the quality of college English teaching to meet students' academic and career development needs. The quality evaluation of college English teaching is multiple-attribute group decision-making (MAGDM). To address this, combined TODIM (Logarithmic TODIM and Exponential TODIM) and PROMETHEE approaches are utilized to propose a MAGDM framework. Considering the need to capture fuzzy information during the quality evaluation process, probabilistic linguistic term sets (PLTSs) are employed. In this study, we construct the probabilistic linguistic combined TODIM-PROMETHEE (PL-Com-TODIM-PROMETHEE) approach to tackle MAGDM under PLTSs. To determine the weight values within the PLTSs framework, we employ the MEREC approach. Finally, a numerical example is presented to validate the effectiveness of the PL-Com-TODIM-PROMETHEE approach for quality evaluation of college English teaching. Through this approach, the study contributes to the advancement of quality evaluation methodologies by integrating combined TODIM and PROMETHEE within the PLTSs framework. It addresses the challenges posed by fuzzy information and provides a practical and effective approach for decision-making in the context of quality evaluation of college English teaching.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200191"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148528","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

Interior design assistant algorithm based on indoor scene analysis

Systems and Soft Computing

Pub Date : 2025-01-17 DOI: 10.1016/j.sasc.2025.200190

Lu Zhang

The scene analysis algorithm in interior design is widely used in computer vision. To achieve superior interior design outcomes, it is essential to accurately identify and locate indoor objects and structures. However, the common algorithms currently rely too much on color images and manual annotation. Accordingly, the objective of the research is to enhance the interior scene analysis algorithm in interior design, thereby optimizing its performance in the domain of computer vision. In light of the shortcomings of existing algorithms that rely excessively on color images and manually labeled data, this paper employs a dual feature encoder to conduct a comprehensive mining of deep image features, thereby markedly enhancing the precision of semantic segmentation. Then, the accuracy of indoor scene analysis is further improved by integrating the texture features of color images into the modal knowledge distillation of depth images. In addition, to reduce the dependence on manually labeled data, an unsupervised cooperative segmentation algorithm is proposed, which realizes automatic image semantic segmentation through the segmentation process from superpixel to block and then to object. The experimental results showed that the proposed algorithm based on modal knowledge distillation had an average accuracy of 48.29 % in the four types of output. The FIoU value of the unsupervised image cooperative segmentation algorithm reached 66.20, which is superior to the existing algorithms and can better match the real indoor scene. The proposed indoor scene analysis algorithm using color images as privileged information significantly improves the accuracy of indoor scene analysis and reduces reliance on manually annotated data. Moreover, the research algorithm effectively identifies indoor objects, protects personal privacy, and provides a better solution for indoor object analysis.

{"title":"Interior design assistant algorithm based on indoor scene analysis","authors":"Lu Zhang","doi":"10.1016/j.sasc.2025.200190","DOIUrl":"10.1016/j.sasc.2025.200190","url":null,"abstract":"<div><div>The scene analysis algorithm in interior design is widely used in computer vision. To achieve superior interior design outcomes, it is essential to accurately identify and locate indoor objects and structures. However, the common algorithms currently rely too much on color images and manual annotation. Accordingly, the objective of the research is to enhance the interior scene analysis algorithm in interior design, thereby optimizing its performance in the domain of computer vision. In light of the shortcomings of existing algorithms that rely excessively on color images and manually labeled data, this paper employs a dual feature encoder to conduct a comprehensive mining of deep image features, thereby markedly enhancing the precision of semantic segmentation. Then, the accuracy of indoor scene analysis is further improved by integrating the texture features of color images into the modal knowledge distillation of depth images. In addition, to reduce the dependence on manually labeled data, an unsupervised cooperative segmentation algorithm is proposed, which realizes automatic image semantic segmentation through the segmentation process from superpixel to block and then to object. The experimental results showed that the proposed algorithm based on modal knowledge distillation had an average accuracy of 48.29 % in the four types of output. The FIoU value of the unsupervised image cooperative segmentation algorithm reached 66.20, which is superior to the existing algorithms and can better match the real indoor scene. The proposed indoor scene analysis algorithm using color images as privileged information significantly improves the accuracy of indoor scene analysis and reduces reliance on manually annotated data. Moreover, the research algorithm effectively identifies indoor objects, protects personal privacy, and provides a better solution for indoor object analysis.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200190"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149505","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

Sentiment analysis of movie reviews: A flask application using CNN with RoBERTa embeddings

Systems and Soft Computing

Pub Date : 2025-01-17 DOI: 10.1016/j.sasc.2025.200192

Biplov Paneru , Bipul Thapa , Bishwash Paneru

Sentiment analysis, an important task in Natural Language Processing (NLP), focuses on identifying and extracting sentiments from input. With the exponential expansion of digital information, sentiment analysis has recently gained significant attention across various domains. Traditional sentiment analysis methods paired with static embeddings often fall short in capturing the deep contextual relationships within text. In this work, we analyze sentiment in IMDB movie reviews using a hybrid deep learning model combining RoBERTa embeddings with a convolutional neural network (R-CNN). We provide a comprehensive overview of the creation and assessment of a convolutional learning model especially suited for sentiment analysis of movie reviews using a dataset of around 50k entries. The proposed approach preprocesses movie reviews, employs RoBERTa to generate rich contextual embeddings, and processes these embeddings through a simple yet effective R-CNN architecture. We perform comprehensive analysis of the R-CNN model, showing a superior test accuracy of 91.5 %, achieving the best results compared to the baseline. Additionally, we develop a Flask-based application, demonstrating the practical applicability of our R-CNN model for real-time sentiment prediction.

{"title":"Sentiment analysis of movie reviews: A flask application using CNN with RoBERTa embeddings","authors":"Biplov Paneru , Bipul Thapa , Bishwash Paneru","doi":"10.1016/j.sasc.2025.200192","DOIUrl":"10.1016/j.sasc.2025.200192","url":null,"abstract":"<div><div>Sentiment analysis, an important task in Natural Language Processing (NLP), focuses on identifying and extracting sentiments from input. With the exponential expansion of digital information, sentiment analysis has recently gained significant attention across various domains. Traditional sentiment analysis methods paired with static embeddings often fall short in capturing the deep contextual relationships within text. In this work, we analyze sentiment in IMDB movie reviews using a hybrid deep learning model combining RoBERTa embeddings with a convolutional neural network (R-CNN). We provide a comprehensive overview of the creation and assessment of a convolutional learning model especially suited for sentiment analysis of movie reviews using a dataset of around 50k entries. The proposed approach preprocesses movie reviews, employs RoBERTa to generate rich contextual embeddings, and processes these embeddings through a simple yet effective R-CNN architecture. We perform comprehensive analysis of the R-CNN model, showing a superior test accuracy of 91.5 %, achieving the best results compared to the baseline. Additionally, we develop a Flask-based application, demonstrating the practical applicability of our R-CNN model for real-time sentiment prediction.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200192"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149507","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 visual synchronous positioning and mapping technology assisted by ultra wideband positioning technology

Systems and Soft Computing

Pub Date : 2025-01-17 DOI: 10.1016/j.sasc.2025.200187

Yiran Zhang, Lina Dong

With the development of the intelligent era, improving the positioning accuracy and operational stability of robots has become an urgent problem that needs to be solved. This study combines the advantages and disadvantages of visual synchronous positioning and mapping technology, inertial measurement units, and ultra-wideband technology to design a combined positioning system. The system first uses the pre-integration method of the inertial measurement unit to align the inertial measurement unit with the camera frequency. Then, it uses a tightly coupled method to fuse the measurement data of the system and the inertial measurement unit, forming a visual-inertial system. The study uses extended Kalman filtering to fuse the constructed visual-inertial system with ultra-wideband technology, creating an ultra-wideband/visual-inertial integrated system. Finally, simulation analysis was conducted on the constructed composite system. The results indicated that the RMSE of the ultra-wideband/visual-inertial system under light and dark conditions were 0.0123 and 0.0212, and 0.0114 and 0.0123, respectively, in the motion trajectories with and without forming a loop. In extremely complex motion trajectories, the RMSE error of the research system was 0.0123. This indicates that regardless of the conditions, the research system has long-term robustness and high-precision positioning performance.

{"title":"Research and application of visual synchronous positioning and mapping technology assisted by ultra wideband positioning technology","authors":"Yiran Zhang, Lina Dong","doi":"10.1016/j.sasc.2025.200187","DOIUrl":"10.1016/j.sasc.2025.200187","url":null,"abstract":"<div><div>With the development of the intelligent era, improving the positioning accuracy and operational stability of robots has become an urgent problem that needs to be solved. This study combines the advantages and disadvantages of visual synchronous positioning and mapping technology, inertial measurement units, and ultra-wideband technology to design a combined positioning system. The system first uses the pre-integration method of the inertial measurement unit to align the inertial measurement unit with the camera frequency. Then, it uses a tightly coupled method to fuse the measurement data of the system and the inertial measurement unit, forming a visual-inertial system. The study uses extended Kalman filtering to fuse the constructed visual-inertial system with ultra-wideband technology, creating an ultra-wideband/visual-inertial integrated system. Finally, simulation analysis was conducted on the constructed composite system. The results indicated that the RMSE of the ultra-wideband/visual-inertial system under light and dark conditions were 0.0123 and 0.0212, and 0.0114 and 0.0123, respectively, in the motion trajectories with and without forming a loop. In extremely complex motion trajectories, the RMSE error of the research system was 0.0123. This indicates that regardless of the conditions, the research system has long-term robustness and high-precision positioning performance.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200187"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149506","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

A dynamic financial risk prediction system for enterprises based on gradient boosting decision tree algorithm

Systems and Soft Computing

Pub Date : 2025-01-17 DOI: 10.1016/j.sasc.2025.200189

Lin Ji , Shenglu Li

As financial technology develops, the dynamic prediction of enterprise financial risks has become a focus of attention in the financial field. The research aims to construct a dynamic financial risk prediction system for enterprises based on gradient boosting decision trees to improve the predicting accuracy and adaptability. The minimum absolute value shrinkage and selection operator algorithm were used for dynamic indicator selection. A dynamic prediction model was constructed by combining gradient boosting decision trees. The decision tree model parameters were optimized through gradient optimization using the sparrow search algorithm. The integrated model performed excellently on multiple evaluation indicators, with an area under the receiver operating characteristic curve of 0.8. The average accuracy was 92.38%, the recall was 94.27%, and the root mean square error and average absolute error were lower than other models, demonstrating high prediction accuracy and reliability. The average user satisfaction of this integrated model was 85%, significantly higher than the 46% of the ordinary gradient boosting decision tree model. This model can not only accurately identify risk situations, but also meet the actual needs of enterprise users. This study provides a new financial risk assessment tool for enterprises. This helps enterprises to identify and manage potential risks in a timely manner, which is of great significance for promoting healthy and sustainable development of enterprises.

{"title":"A dynamic financial risk prediction system for enterprises based on gradient boosting decision tree algorithm","authors":"Lin Ji , Shenglu Li","doi":"10.1016/j.sasc.2025.200189","DOIUrl":"10.1016/j.sasc.2025.200189","url":null,"abstract":"<div><div>As financial technology develops, the dynamic prediction of enterprise financial risks has become a focus of attention in the financial field. The research aims to construct a dynamic financial risk prediction system for enterprises based on gradient boosting decision trees to improve the predicting accuracy and adaptability. The minimum absolute value shrinkage and selection operator algorithm were used for dynamic indicator selection. A dynamic prediction model was constructed by combining gradient boosting decision trees. The decision tree model parameters were optimized through gradient optimization using the sparrow search algorithm. The integrated model performed excellently on multiple evaluation indicators, with an area under the receiver operating characteristic curve of 0.8. The average accuracy was 92.38%, the recall was 94.27%, and the root mean square error and average absolute error were lower than other models, demonstrating high prediction accuracy and reliability. The average user satisfaction of this integrated model was 85%, significantly higher than the 46% of the ordinary gradient boosting decision tree model. This model can not only accurately identify risk situations, but also meet the actual needs of enterprise users. This study provides a new financial risk assessment tool for enterprises. This helps enterprises to identify and manage potential risks in a timely manner, which is of great significance for promoting healthy and sustainable development of enterprises.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200189"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149508","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

Real-time facial reconstruction and expression replacement based on neural radiation field

Systems and Soft Computing

Pub Date : 2025-01-04 DOI: 10.1016/j.sasc.2025.200185

Shenning Zhang , Hui Li , Xuefeng Tian

It is now possible to do high-fidelity 3D facial reconstruction and unique view synthesis thanks to the recent discovery of Neural Radiance Fields (NeRF), which has established its substantial importance in the field of 3D vision. However, the operational approaches that are now in use require a significant amount of human engagement, such as the need for users to provide semantic masks and the inconvenience of manual attribute searching for non-expert users. Our approach focuses on enabling the manipulation of NeRF-reconstructed faces with just a single text input. A scene manipulator, specifically a conditional version NeRF with deformable latent codes, is the first thing that this paper trains to accomplish this objective, in dynamic scenes, allowing facial deformations to be controlled through latent codes. However, to synthesize local deformations in a variety of contexts, it is not desirable to describe scene deformations using only a single latent coding. Therefore, this paper proposes a text-driven operation pipeline for facial reconstruction with NeRF, the development of an operating network that is capable of learning to represent scene changes using latent codes that vary at different spatial locations, and the integration of a WeChat mini-program to facilitate practical applications. This application approach enables even non-expert users to easily synthesize novel views. Our method has achieved a certain breakthrough in the field of 3D facial reconstruction, providing users with a simple and convenient text-driven operation approach.

{"title":"Real-time facial reconstruction and expression replacement based on neural radiation field","authors":"Shenning Zhang , Hui Li , Xuefeng Tian","doi":"10.1016/j.sasc.2025.200185","DOIUrl":"10.1016/j.sasc.2025.200185","url":null,"abstract":"<div><div>It is now possible to do high-fidelity 3D facial reconstruction and unique view synthesis thanks to the recent discovery of Neural Radiance Fields (NeRF), which has established its substantial importance in the field of 3D vision. However, the operational approaches that are now in use require a significant amount of human engagement, such as the need for users to provide semantic masks and the inconvenience of manual attribute searching for non-expert users. Our approach focuses on enabling the manipulation of NeRF-reconstructed faces with just a single text input. A scene manipulator, specifically a conditional version NeRF with deformable latent codes, is the first thing that this paper trains to accomplish this objective, in dynamic scenes, allowing facial deformations to be controlled through latent codes. However, to synthesize local deformations in a variety of contexts, it is not desirable to describe scene deformations using only a single latent coding. Therefore, this paper proposes a text-driven operation pipeline for facial reconstruction with NeRF, the development of an operating network that is capable of learning to represent scene changes using latent codes that vary at different spatial locations, and the integration of a WeChat mini-program to facilitate practical applications. This application approach enables even non-expert users to easily synthesize novel views. Our method has achieved a certain breakthrough in the field of 3D facial reconstruction, providing users with a simple and convenient text-driven operation approach.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200185"},"PeriodicalIF":0.0,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148526","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

Local image style transfer algorithm for personalized clothing customization design

Systems and Soft Computing

Pub Date : 2025-01-02 DOI: 10.1016/j.sasc.2025.200183

Xuemeng Wu

With the increasing demand for personalized clothing from consumers, the style transfer technology of clothing images has become a key link in clothing customization design. However, when transferring clothing image styles in complex backgrounds, problems such as poor local image style transfer and boundary artifacts often arise. To address these issues, an attention mechanism-based approach to local style transfer in cyclic generative adversarial networks has been proposed. By introducing attention mechanisms, more precise probability allocation has been achieved. In addition, this study designs a local artifact correction model based on an improved residual network. The experimental results showed that the proposed method had an average ratio of 0.83 for the best performance image in user perception evaluation, which was at least 23.9 % higher than other methods. In addition, the average distance similarity of this research method reached 0.244, which was at least 4.4 % higher than other methods. In terms of mean square error, the research method had a mean square error as low as 3426, which was at least 8.5 % lower than other algorithms. In addition, regarding the artifact correction part, the average opinion score of the proposed method was 2.9, which was at least 7.4 % higher than other algorithms. The mean square error of this algorithm was only 16.13, at least 34.2 % lower than other algorithms. This study verifies the effectiveness of the proposed method in local style transfer and artifact correction of clothing images, provides strong technical support for the field of clothing customization, and helps to promote technical progress in this field.

{"title":"Local image style transfer algorithm for personalized clothing customization design","authors":"Xuemeng Wu","doi":"10.1016/j.sasc.2025.200183","DOIUrl":"10.1016/j.sasc.2025.200183","url":null,"abstract":"<div><div>With the increasing demand for personalized clothing from consumers, the style transfer technology of clothing images has become a key link in clothing customization design. However, when transferring clothing image styles in complex backgrounds, problems such as poor local image style transfer and boundary artifacts often arise. To address these issues, an attention mechanism-based approach to local style transfer in cyclic generative adversarial networks has been proposed. By introducing attention mechanisms, more precise probability allocation has been achieved. In addition, this study designs a local artifact correction model based on an improved residual network. The experimental results showed that the proposed method had an average ratio of 0.83 for the best performance image in user perception evaluation, which was at least 23.9 % higher than other methods. In addition, the average distance similarity of this research method reached 0.244, which was at least 4.4 % higher than other methods. In terms of mean square error, the research method had a mean square error as low as 3426, which was at least 8.5 % lower than other algorithms. In addition, regarding the artifact correction part, the average opinion score of the proposed method was 2.9, which was at least 7.4 % higher than other algorithms. The mean square error of this algorithm was only 16.13, at least 34.2 % lower than other algorithms. This study verifies the effectiveness of the proposed method in local style transfer and artifact correction of clothing images, provides strong technical support for the field of clothing customization, and helps to promote technical progress in this field.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200183"},"PeriodicalIF":0.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148529","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

Application of hybrid ant colony algorithm to the design of analog wavelet optimized circuits

Systems and Soft Computing

Pub Date : 2025-01-02 DOI: 10.1016/j.sasc.2025.200184

Quanhui Ren, Chenyu Meng

Facing the real-time and low-power requirements in non-smooth signal processing, the traditional digital wavelet transform method limits its efficiency and feasibility in practical applications due to the large amount of arithmetic and the need for A/D conversion. In order to overcome these shortcomings, the study proposes an analog circuit design method for rational approximation of wavelet function using hybrid ant colony algorithm. The study performs constrained mathematical modeling of the wavelet approximation through the minimum mean square error criterion and optimizes it using the hybrid ant colony algorithm. Also, the study designs a current-mode circuit based on the operational transconductance amplifier and current controlled conveyor second generation for implementing the analog wavelet transform. The results revealed that the amplitude response of the hybrid ant colony algorithm optimized analog wavelet circuit design reached 0.93 with an error of only 3.33%. In conclusion, it can be concluded that the research on the application of hybrid ant colony algorithm in the design of analog wavelet optimized circuits effectively improves the accuracy of wavelet approximation, and provides a new technological path for the realization of highly efficient and low-cost signal processing circuits.

{"title":"Application of hybrid ant colony algorithm to the design of analog wavelet optimized circuits","authors":"Quanhui Ren, Chenyu Meng","doi":"10.1016/j.sasc.2025.200184","DOIUrl":"10.1016/j.sasc.2025.200184","url":null,"abstract":"<div><div>Facing the real-time and low-power requirements in non-smooth signal processing, the traditional digital wavelet transform method limits its efficiency and feasibility in practical applications due to the large amount of arithmetic and the need for A/D conversion. In order to overcome these shortcomings, the study proposes an analog circuit design method for rational approximation of wavelet function using hybrid ant colony algorithm. The study performs constrained mathematical modeling of the wavelet approximation through the minimum mean square error criterion and optimizes it using the hybrid ant colony algorithm. Also, the study designs a current-mode circuit based on the operational transconductance amplifier and current controlled conveyor second generation for implementing the analog wavelet transform. The results revealed that the amplitude response of the hybrid ant colony algorithm optimized analog wavelet circuit design reached 0.93 with an error of only 3.33%. In conclusion, it can be concluded that the research on the application of hybrid ant colony algorithm in the design of analog wavelet optimized circuits effectively improves the accuracy of wavelet approximation, and provides a new technological path for the realization of highly efficient and low-cost signal processing circuits.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200184"},"PeriodicalIF":0.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148530","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

Cascaded intrusion detection system using machine learning

Systems and Soft Computing

Pub Date : 2025-01-01 DOI: 10.1016/j.sasc.2024.200182

Md. Khabir Uddin Ahamed , Abdul Karim

Cybercrime is becoming an increasing concern these days. In response to the growing cyberthreat, various intrusion detection systems have been developed and proposed to detect anomalies. However, most detection systems suffer from some common issues, such as a high number of false positives that cause regular behaviors to be detected as intrusions, as well as the system’s excessive complexity. Many single classifier models have accuracy issues since they are unable to detect certain anomalies caused by the attack’s polymorphic and zero-day behavior. The signature-based intrusion detection system (SIDS) is unable to identify zero-day intrusions. On the other side, the anomaly-based intrusion detection system (AIDS) generates a significant number of false-positive alarms. In this research, a cascaded intrusion detection system (CIDS) is proposed by combining the one-class support vector machine (OC-SVM)-based AIDS and the decision tree-based SIDS. OC-SVM is used in conjunction with the newly built Distance-Based Intrusion Classification System (DICS). SIDS that use decision trees can discover and classify anomalies. Because OC-SVM is a binary classifier, the intrusion type is determined by DICS. The suggested method aims to detect both popular and well-known zero-day attacks, as well as their type. The CIDS is evaluated using publicly available benchmark datasets, such as the Knowledge Discovery in Databases (KDD) Cup 1999 and the NSL-KDD dataset. The results of the proposed study show that CIDS outperformed both traditional SIDS and AIDS in terms of performance. Both anomalies and their types are detected with high accuracy.

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