Systems and Soft Computing最新文献

{"title":"Intelligent long jump evaluation system integrating blazepose human pose assessment algorithm in higher education sports teaching","authors":"Tao Wang","doi":"10.1016/j.sasc.2024.200130","DOIUrl":"10.1016/j.sasc.2024.200130","url":null,"abstract":"<div><p>There are issues in current higher education long jump teaching, e.g., assessment relies on teachers' experience, lacks scientific evaluation, and can't quantitatively give performance feedback to students. To address these issues, this research first divides the long jump process into the approach run and mid-air phases. Secondly, it proposes a method for measuring approach run speed based on virtual line velocity algorithm. Subsequently, by combining the BlazePose human pose assessment algorithm with posture matching algorithms, a technique for assessing mid-air long jump movements integrated with BlazePose human pose assessment algorithm is designed. Finally, an intelligent long jump evaluation system incorporating the BlazePose human pose assessment algorithm is established. The research findings demonstrate that the average accuracy of video at 120FPS reaches a maximum of 94.47%. The assessment accuracy of mid-air long jump movements integrated with the BlazePose human pose assessment algorithm is highest, with accuracies of 94%, 90%, and 88% for the takeoff, hip extension, and abdominal contraction key movements respectively. Additionally, the method shows a scoring result with an average error range of 3 points compared to evaluations by professional teachers. In the practical application of the BlazePose human pose assessment algorithm's intelligent long jump evaluation system, evaluation scores and long jump proficiency receive scientifically objective assessments, while teachers provide targeted corrective feedback, achieving good application results. In summary, the proposed intelligent long jump evaluation system exhibits good performance, complete functionality, and can provide quantifiable data references for both teachers and students.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200130"},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000590/pdfft?md5=8c0d58ff3aa370f8ea4af56703cbb005&pid=1-s2.0-S2772941924000590-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985455","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":"Posture detection of athletes in sports based on posture solving algorithms","authors":"Huan Zhang","doi":"10.1016/j.sasc.2024.200128","DOIUrl":"10.1016/j.sasc.2024.200128","url":null,"abstract":"<div><p>With the rapid development of science and technology, the field of sports is constantly exploring and applying new technical means to improve the training effect and competitive level of athletes. Among them, the athletes' posture detection technology based on the attitude solving algorithm has been widely concerned in recent years. However, the current attitude solving algorithm has the limitation of low precision and low efficiency. Aiming at this, a new attitude solving algorithm is proposed. Firstly, the coordinate system is determined according to the theory of inertial navigation, and the attitude Angle is obtained by calculating the acceleration and magnetic induction intensity. Then the current attitude matrix is calculated according to the obtained attitude Angle. The initializing quaternion based on the attitude matrix is studied. Then, according to the advantages and defects of the three sensors, a complementary filtering algorithm is proposed for data fusion, so as to reduce the error of the final attitude solution. In order to further improve the accuracy of attitude detection, the complementary filter algorithm and double-layer Kalman filter algorithm are combined to process the data, and finally the quaternion is updated. It can be seen that the detection error of the research constructed model is only 9.94%, and its three attitude angle errors are mainly concentrated between -0.5° and 0.5° The model constructed by the research can realize high-precision posture detection, which can provide more scientific and reliable training aids for gymnastics, which has very strict requirements for movements in sports. It has positive significance for the development of sports.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200128"},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000577/pdfft?md5=13f1d1693e2079aacb2a02a0d0deb340&pid=1-s2.0-S2772941924000577-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962189","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":"Integrating convolutional neural networks for microscopic image analysis in acute lymphoblastic leukemia classification: A deep learning approach for enhanced diagnostic precision","authors":"Md. Samiul Alim ,&nbsp;Suborno Deb Bappon ,&nbsp;Shahriar Mahmud Sabuj ,&nbsp;Md Jayedul Islam ,&nbsp;M. Masud Tarek ,&nbsp;Md. Shafiul Azam ,&nbsp;Md. Monirul Islam","doi":"10.1016/j.sasc.2024.200121","DOIUrl":"10.1016/j.sasc.2024.200121","url":null,"abstract":"<div><p>Leukemia is a type of cancer characterized by the exponential growth of abnormal blood cells, which damages white blood cells and disrupts the function of the human body’s bone marrow. It is very challenging to classify because blood smear images are complicated, and there is a lot of variation between each class. Acute Lymphoblastic Leukemia (B-ALL) is one of the subtypes of leukemia. It is a rapidly progressing cancer that originates in B lymphocytes, characterized by the overproduction of immature B lymphoblasts. The purpose of this work is to classify different types of B-ALL subtypes such as Benign, Malignant Early Pre-B, Malignant Pre-B, and Malignant Pro-B from the peripheral blood smear images effectively. To accomplish this task, a novel deep-learning technique based on a fine-tuned ResNet-50 model has been developed. Our fine-tuned ResNet-50 model integrates several additional customized fully connected layers, including dense and dropout layers. Various data augmentation techniques such as flipping, rotation, and zooming have been applied to mitigate the risk of overfitting. In addition, a five-fold cross-validation technique has been employed to enhance the model’s generalization. The performance of our proposed technique is compared with several other methods, including VGG-16, DenseNet-121, and EfficientNetB0, as well as existing baselines, using different performance metrics. Experimental results demonstrate the superiority of the fine-tuned ResNet-50 model, achieving the highest accuracy and an F1-score of 99.38%. It also outperforms existing state-of-the-art approaches by a significant margin. The proposed fine-tuned ReNet-50 model achieves such performance without the need for microscopic image segmentation which indicates its potential utility in healthcare sectors in enhancing precise leukemia diagnosis.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200121"},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000504/pdfft?md5=fa4cc0c57d83eedef0387dd9a704c4b5&pid=1-s2.0-S2772941924000504-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962190","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":"Big data technology for teaching quality monitoring and improvement in higher education - joint K-means clustering algorithm and Apriori algorithm","authors":"Yang Li,&nbsp;Haiyu Zhang","doi":"10.1016/j.sasc.2024.200125","DOIUrl":"10.1016/j.sasc.2024.200125","url":null,"abstract":"<div><p>With the development of big data technology, the field of monitoring and improving teaching quality in universities has ushered in new opportunities and challenges. Big data technology enables the capture and analysis of massive amounts of data generated during the teaching process, providing the possibility for a deeper understanding of teaching activities. However, how to extract useful information from these vast amounts of data and transform it into strategies for teaching improvement is a challenge. The research aims to propose a teaching quality monitoring and improvement method based on big data technology, which combines K-means clustering algorithm and association rule mining algorithm to improve the accuracy of teaching monitoring and the effectiveness of teaching improvement. In order to cope with these challenges, the study proposes a research method of big data technology based on joint K-mean clustering algorithm and association rule mining algorithm. The study first analyzes the teaching quality monitoring and evaluation indexes using the K-mean algorithm. Then the association rule mining algorithm is utilized to mine the data in the teaching quality monitoring indicators with association rules on the basis of the obtained cluster analysis. Finally, on the basis of association rule mining, the study constructs the assessment model of teaching quality monitoring indicators by utilizing the fused method. The outcomes revealed that the average of data analysis accuracy and the average of recall rate of the modeling method were 93.79 % and 91.95 %, respectively. Meanwhile, the evaluation time of the modeling method in the process of teaching quality monitoring data processing was 17.3 s, and the evaluation precision was 93.15 % respectively. Additionally, the process's overall confidence and enhancement are 95.01 % and 86.73 %, respectively, and the modeling method's performance is compared to other approaches. This shown that the approach may significantly boost the precision and effectiveness of monitoring the quality of instruction, as well as offer strong backing for the enhancement of instruction in higher education institutions.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200125"},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000541/pdfft?md5=03f60d5d6d7c22a4a0fd1e8781919d99&pid=1-s2.0-S2772941924000541-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962758","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":"Design of a logistics warehouse robot positioning and recognition model based on improved EKF and calibration algorithm","authors":"Yunbo Wang,&nbsp;Chao Ye","doi":"10.1016/j.sasc.2024.200127","DOIUrl":"10.1016/j.sasc.2024.200127","url":null,"abstract":"<div><p>Automatic guided vehicles for logistics warehousing are a key link in the construction of intelligent logistics. To improve the positioning accuracy of warehouse robots, we designed an advanced extended Kalman filter method integrating multiple synchronous positioning techniques and map construction methods, and completed the calibration and detection of pallets based on color image information. The results revealed that the proposed multi-innovation enhanced model achieved minimum relative rotation and absolute trajectory errors of 0.13 and 0.09, outperforming existing models. It showcased excellent mapping fidelity and integrity (above 0.9) across various datasets, with a high loop detection success rate (0.91) enhancing map precision. The tray fusion detection algorithm's AUC (area under the curve) reached 0.92, reflecting a balanced accuracy-recall tradeoff. This research offers robust positioning and mapping capabilities in logistics warehousing environments, effectively identifying errors and ensuring pallet accuracy. The detection error and accuracy of this method are better than the other three models, with the lowest average absolute error of 0.32 and the lowest root-mean-square error of 0.27, and the overall error in the detection of pallets is small. The findings provide strong theoretical backing and technical support for advancing intelligent logistics warehousing technology. Precise positioning and identification capabilities enable logistics and warehousing robots to accurately and quickly complete tasks such as access, handling and sorting of goods, greatly improving the efficiency of warehousing operations, promoting the digital transformation and intelligent development of the logistics and warehousing industry, and improving the competitiveness of the industry and the level of service.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200127"},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000565/pdfft?md5=4dab2010b194ed6fc11a06a186b512c4&pid=1-s2.0-S2772941924000565-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141952240","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":"Multi-objective optimization analysis of construction management site layout based on improved genetic algorithm","authors":"Hui Yin","doi":"10.1016/j.sasc.2024.200113","DOIUrl":"10.1016/j.sasc.2024.200113","url":null,"abstract":"<div><p>In construction management, the rationality of on-site layout is crucial for project progress, cost, and safety. In order to improve the rationality of on-site layout, a multi-objective optimization model combining ant colony algorithm and Pareto optimal solution was constructed based on genetic algorithm, and this model was applied to practical engineering cases. The results show that in terms of computational time, the genetic algorithm takes an average of 1702.0 s, while the improved algorithm takes an average of 421.0 s, which is 1281s less and 85.9% more than before the improvement. The performance of the improved algorithm is the best, and the optimal solution can be obtained through multiple iterations. The improved algorithm has improved the efficiency of on-site layout optimization, and possesses practical application value for the layout of construction management sites. It offers a certain reference for the reasonable setting of construction management sites.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200113"},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000425/pdfft?md5=7d39ecb1f21c34a9dcdedf6dbf642fae&pid=1-s2.0-S2772941924000425-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141840471","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":"Novel hybrid integrated Pix2Pix and WGAN model with Gradient Penalty for binary images denoising","authors":"Luca Tirel ,&nbsp;Ali Mohamed Ali ,&nbsp;Hashim A. Hashim","doi":"10.1016/j.sasc.2024.200122","DOIUrl":"10.1016/j.sasc.2024.200122","url":null,"abstract":"<div><p>This paper introduces a novel approach to image denoising that leverages the advantages of Generative Adversarial Networks (GANs). Specifically, we propose a model that combines elements of the Pix2Pix model and the Wasserstein GAN (WGAN) with Gradient Penalty (WGAN-GP). This hybrid framework seeks to capitalize on the denoising capabilities of conditional GANs, as demonstrated in the Pix2Pix model, while mitigating the need for an exhaustive search for optimal hyperparameters that could potentially ruin the stability of the learning process. In the proposed method, the GAN’s generator is employed to produce denoised images, harnessing the power of a conditional GAN for noise reduction. Simultaneously, the implementation of the Lipschitz continuity constraint during updates, as featured in WGAN-GP, aids in reducing susceptibility to mode collapse. This innovative design allows the proposed model to benefit from the strong points of both Pix2Pix and WGAN-GP, generating superior denoising results while ensuring training stability. Drawing on previous work on image-to-image translation and GAN stabilization techniques, the proposed research highlights the potential of GANs as a general-purpose solution for denoising. The paper details the development and testing of this model, showcasing its effectiveness through numerical experiments. The dataset was created by adding synthetic noise to clean images. Numerical results based on real-world dataset validation underscore the efficacy of this approach in image-denoising tasks, exhibiting significant enhancements over traditional techniques. Notably, the proposed model demonstrates strong generalization capabilities, performing effectively even when trained with synthetic noise.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200122"},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000516/pdfft?md5=659c9386959ee51d647277145e4cf8b4&pid=1-s2.0-S2772941924000516-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141852945","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":"Hybrid procurement model for the construction of library literature and information resource procurement","authors":"Chuanyu Zhang,&nbsp;Changsheng Wang","doi":"10.1016/j.sasc.2024.200124","DOIUrl":"10.1016/j.sasc.2024.200124","url":null,"abstract":"<div><p>To improve the efficiency of intelligent procurement of library literature and intelligence resources, the study conducts the design of literature and intelligence resources procurement model. The procurement model is constructed by using the support vector machine, and the optimal parameters of the support vector machine are obtained by using the genetic algorithm. The experimental results demonstrated that the mean square error of the proposed model was only 0.03, which was 40 % lower compared with the procurement models based on other optimization algorithms. The average accuracy of the proposed model was as high as 95.18 % and the prediction accuracy was 95.78 % compared to other methods. The accuracy was improved by 15.11 %, 24.57 % and 19.67 % respectively compared to other models. The results show that using genetic algorithm to optimize support vector machine can effectively improve the prediction speed and prediction efficiency of the model. The proposed hybrid procurement model based on genetic algorithm and support vector machine can effectively meet the needs of library literature and intelligence resources procurement construction. The model has positive application significance in library literature and intelligence resources procurement.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200124"},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277294192400053X/pdfft?md5=616b8e182067131edcffc30542433f60&pid=1-s2.0-S277294192400053X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141850261","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":"Color image hybrid noise filtering algorithm based on deep convolution neural network","authors":"Yongfei Yu ,&nbsp;Yuanjian Yan","doi":"10.1016/j.sasc.2024.200120","DOIUrl":"10.1016/j.sasc.2024.200120","url":null,"abstract":"<div><p>To solve the problems of the classical color image hybrid noise filtering method, a deep convolutional neural network improved by evolutionary strategy and jump connection is proposed and applied to the filtering noise reduction of color images. First, the color information of the image is described quantitatively by digital means. The common method is to build color space model. According to the characteristics of color and the needs of human vision, mathematical algorithms are used to convert images into machine recognizable data. The distance between pixels is measured according to the difference of pixels in the color image determined above. Then, the probability density function and noise probability density function of Gaussian noise are calculated to determine the hybrid noise feature points of color image. The filtering algorithm structure designed this time is as follows: A color image hybrid noise filter is used to map the noise points in the mapped image to the feature space, and linear regression is performed on the noise point data. Relaxation variables are introduced in the network to improve the denoising ability. The experimental results show that the Peak Signal to Noise Ratio and structural similarity index values of the filtering algorithm designed in this study are higher than the two methods in the literature. The color image hybrid noise filtering model designed in this study has good filtering performance, good image cleanliness, and high filtering efficiency.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200120"},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000498/pdfft?md5=c1dd2d6f7dfd2b611b79111c8412755c&pid=1-s2.0-S2772941924000498-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141712122","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":"Furniture design based on image color extraction algorithm","authors":"Binglu Chen ,&nbsp;Guanyu Chen ,&nbsp;Qianqian Hu","doi":"10.1016/j.sasc.2024.200123","DOIUrl":"10.1016/j.sasc.2024.200123","url":null,"abstract":"<div><p>With the increasing demand for personalized and customized home products, how to realize the innovative design of furniture and improve the design efficiency has become a research hotspot for related professionals. Aiming at these problems, the study extracts the main color of furniture images by optimizing the K-mean clustering algorithm, uses the simulated annealing algorithm to color-match the furniture, and reconstructs the image by edge detection to design a furniture design method based on image color extraction. The results revealed that in the foreground part, the correct rate of color match based on the design method was 95.7%, and in the background part, the correct rate of color match based on the design method was 94.81 %, which proved its effectiveness. The average feature point extraction time and the average feature point matching time of the design-based algorithm were 5.45 ms and 9.83 ms, respectively, which proved its high computational efficiency. In furniture color edge detection and overall color match, the image obtained based on the design method was significantly clearer, and the overall coherence, saturation and brightness were closer to the input image. In addition to raising the standard of furniture design, the study's design methodology increases design efficiency and offers solid technical support for the area.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200123"},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000528/pdfft?md5=ade7c150a82a3798cbeaa5766e1a160b&pid=1-s2.0-S2772941924000528-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141844687","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}