Systems and Soft Computing最新文献

{"title":"Construction of smart tourism system integrating tourist needs and scene characteristics","authors":"Xiqiong Wang","doi":"10.1016/j.sasc.2024.200168","DOIUrl":"10.1016/j.sasc.2024.200168","url":null,"abstract":"<div><div>The demand for travel is increasing as human living conditions rise. The paper presents a smart tourism system architecture that incorporates visitors' demands and scenario characteristics, and performs path planning using path search algorithms and selective tour path recommendation algorithms, in order to improve tourists' travelling experiences and save them time. The experimental data showed that the enhanced heuristic search algorithm visited 122 nodes, which is 62.9% and 52.3% less than the sparrow search algorithm and the improved genetic search strategy, respectively. The number of iterations required to reach convergence for the selective tour path recommendation algorithm, genetic algorithm, discrete particle swarm algorithm, and genetic particle swarm algorithm, respectively, was 39, 90, 85, and 63, indicating that the proposed selective tour path recommendation algorithm has the fastest computational speed. The accuracy, stability, user satisfaction, and overall rating of the smart tourism system that integrates tourists' needs and scenario characteristics are all higher than those of the three types of tourism systems, such as the iBeacon Smart Tourism System, indicating that this smart tourism system is the best to use, helping to enhance tourists' experiences and promote the robust development of the tourism industry.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200168"},"PeriodicalIF":0.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657125","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 enhancement algorithm combining histogram equalization and bilateral filtering","authors":"Mingzhu Wu ,&nbsp;Qiuyan Zhong","doi":"10.1016/j.sasc.2024.200169","DOIUrl":"10.1016/j.sasc.2024.200169","url":null,"abstract":"<div><div>In the process of image acquisition, transmission, and storage, the image quality is often degraded due to a variety of unfavorable factors, resulting in information loss, which poses certain difficulties for subsequent image processing and analysis. How to enhance the visibility of image details and maintain the naturalness of the image is one of the important challenges in image processing. In response to this challenge, an image enhancement algorithm is proposed based on the advantages of histogram equalization and bilateral filtering. This algorithm organically integrates histogram equalization and bilateral filtering, aiming to improve image quality while reducing noise in the image. Specifically, the study first utilizes an improved histogram equalization strategy to preprocess the image and then applies a bilateral filter for further optimization. The experimental results showed that the optimized histogram equalization could effectively improve the global contrast of the image and avoid excessive enhancement and gray phenomenon of the image. Moreover, its peak signal-to-noise ratio could reach 0.71. However, bilateral filters showed significant advantages in processing complex data sets, and the peak signal-to-noise ratio could reach 0.95. It illustrated that the optimal research method has obvious advantages in improving image quality and reducing noise. The new enhancement strategy not only significantly improves the global contrast of the image but also preserves the naturalness of the image, providing important technical support for image analysis, machine vision, and artificial intelligence applications.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200169"},"PeriodicalIF":0.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657126","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 similarity-based multi-objective test optimization technique using search algorithm","authors":"Amir Sohail Habib ,&nbsp;Saif Ur Rehman Khan ,&nbsp;Shahid Hussain ,&nbsp;Naseem Ibrahim ,&nbsp;Habib un Nisa ,&nbsp;Abdullah Yousafzai","doi":"10.1016/j.sasc.2024.200164","DOIUrl":"10.1016/j.sasc.2024.200164","url":null,"abstract":"<div><h3>Context:</h3><div>Software undergoes a constant evolution driven by ongoing changes in customer requirements, which enhances the competitive advantage. Regression testing plays a pivotal role by ensuring that modifications have not introduced detrimental effects on the system under test.</div></div><div><h3>Problem:</h3><div>However, regression testing becomes prohibitively expensive as the software grows in complexity and the size of the test suite also expands. Moreover, keeping the test cases up-to-date and managing the relevant test data can become a laborious and challenging task. Hence, it is required to optimize the test suite by finding a diverse subset of test cases having high code coverage, fault-detection rate, and minimal execution time.</div></div><div><h3>Objective:</h3><div>To solve the regression test optimization problem, the researchers have proposed various approaches including greedy algorithms, search-based algorithms, and clustering algorithms. However, existing approaches lack in finding the global optimal solution and are mostly focused on the single-objective test optimization problem. Inspired by this, we propose a Similarity-based Multi-Objective Optimization Technique (SMOOT) for test suite reduction using a Grey Wolf Optimizer (GWO) algorithm. The proposed technique employs different similarity metrics, including Cosine Similarity, Euclidean Distance, Jaccard Similarity, Manhattan Distance, and Minkowski Distance, to evaluate the similarity score of the tests. This ensures a comprehensive assessment of test diversity to achieve high code coverage and fault-detection rate while minimizing the test execution cost.</div></div><div><h3>Method:</h3><div>We evaluated the performance of GWO with state-of-the-art search-based algorithms using three varying types of case studies. Similarly, to evaluate the similarity score of the considered search algorithms, we employed state-of-the-art similarity measures.</div></div><div><h3>Results:</h3><div>The experimental results revealed that GWO significantly outperformed the considered search algorithms by attaining high code coverage and fault-detection rate while minimizing the test execution time. Moreover, we found that GWO attained a higher similarity score than the other considered search algorithms using the employed similarity measures.</div></div><div><h3>Conclusion:</h3><div>Based on the attained results, we believe that the proposed technique could be useful for the researchers and practitioners by effectively handling multi-objective regression test optimization problem.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200164"},"PeriodicalIF":0.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578038","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":"Advancing human-computer interaction: AI-driven translation of American Sign Language to Nepali using convolutional neural networks and text-to-speech conversion application","authors":"Biplov Paneru ,&nbsp;Bishwash Paneru ,&nbsp;Khem Narayan Poudyal","doi":"10.1016/j.sasc.2024.200165","DOIUrl":"10.1016/j.sasc.2024.200165","url":null,"abstract":"<div><div>Advanced technology that serves people with impairments is severely lacking in Nepal, especially when it comes to helping the hearing impaired communicate. Although sign language is one of the oldest and most organic ways to communicate, there aren't many resources available in Nepal to help with the communication gap between Nepali and American Sign Language (ASL). This study investigates the application of Convolutional Neural Networks (CNN) and AI-driven methods for translating ASL into Nepali text and speech to bridge the technical divide. Two pre-trained transfer learning models, ResNet50 and VGG16, were refined to classify ASL signs using extensive ASL image datasets. The system utilizes the Python gTTS package to translate signs into Nepali text and speech, integrating with an OpenCV video input TKinter-based Graphical User Interface (GUI). With both CNN architectures, the model's accuracy of over 99 % allowed for the smooth conversion of ASL to speech output. By providing a workable solution to improve inclusion and communication, the deployment of an AI-driven translation system represents a significant step in lowering the technological obstacles that disabled people in Nepal must overcome.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200165"},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656479","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 intelligent algorithm for object search based on IoT digital images","authors":"Yinghao Li","doi":"10.1016/j.sasc.2024.200161","DOIUrl":"10.1016/j.sasc.2024.200161","url":null,"abstract":"<div><div>With the development of artificial intelligence, traditional object search and image recognition have been replaced by the Internet of Things and artificial intelligence. However, traditional object search algorithms often lack accuracy and low precision. Therefore, this study proposes a new intelligent encryption algorithm to address the issues of insufficient accuracy in object search algorithms and image recognition algorithms. The new algorithm ensures the security of user data and the response efficiency of the model during the conversation process by integrating fully homomorphic encryption technology and dynamic sparse attention mechanism. The dynamic sparse attention mechanism introduced simultaneously improves the model's ability to handle long sequence data by dynamically adjusting attention weights. Experimental results showed that the precision of the proposed algorithm was 0.05 % higher than that of random algorithms and 0.19 % higher than that of sorting algorithms. The recall rate of the proposed algorithm was 0.14 % higher than that of random algorithms and 0.16 % higher than that of sorting algorithms. The research algorithm can identify objects with certain characteristics and is suitable for specific environments, greatly reducing the probability of data leakage in object search and providing new ideas for research in this field.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200161"},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656478","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":"Fuzzy hybrid approach for advanced teaching learning technique with particle swarm optimization in the diagnostic of dengue disease","authors":"Nivedita ,&nbsp;Riddhi Garg ,&nbsp;Seema Agrawal ,&nbsp;Ajendra Sharma ,&nbsp;M.K. Sharma","doi":"10.1016/j.sasc.2024.200160","DOIUrl":"10.1016/j.sasc.2024.200160","url":null,"abstract":"<div><div>Dengue fever is a serious public health issue worldwide, particularly in tropical and subtropical areas. Early detection and accurate diagnosis are essential for effective management and control of the disease. In this study, we present a fuzzy hybrid approach (F-TLBO-APSO) for the detection and diagnosis of dengue disease using an advanced teaching-learning technique with adaptive particle swarm optimization. The proposed method combines the strengths of fuzzy logic, teaching learning-based optimization (TLBO), and adaptive particle swarm optimization (APSO) to improve the accuracy and efficiency of dengue detection based on symptoms. A key challenge addressed is the management of uncertain information existing in the problem. To validate the proposed technique, we applied it to a case study, demonstrating its robustness. The results indicate the versatility of the F-TLBO-APSO algorithm and highlight its value in detecting dengue based on symptoms. Our numerical computations reveal the advantages of the F-TLBO-APSO algorithm compared to TLBO and APSO.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200160"},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593941","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":"The application of improved DTW algorithm in sports posture recognition","authors":"Changjiang Niu","doi":"10.1016/j.sasc.2024.200163","DOIUrl":"10.1016/j.sasc.2024.200163","url":null,"abstract":"<div><div>Sports posture recognition plays a crucial role in modern sports science and training. Posture recognition and analysis plays a positive role in improving sports quality and ensuring sports safety. However, existing recognition technologies still have poor recognition and accuracy in large amounts of posture data. Therefore, to further improve the performance of the existing posture recognition techniques, this study assumes that postures during movement can be effectively represented through the time series of skeletal key points, and the local similarity of these postures can be captured through the Dynamic Time Warping (DTW) algorithm. Based on this assumption, the existing DTW algorithm is improved by introducing the K-Nearest Neighbor (KNN) algorithm and combining it with Principal Component Analysis (PCA) for feature dimensionality reduction. A novel algorithmic model for postures recognition is proposed. The experimental results showed that the improved algorithm performed well in postures recognition rate and accuracy. Especially, when the k value was 5, the recognition rate reached up to 89%, and the accuracy reached 87%. Compared with the existing algorithm, the improved KNN-DTW algorithm has significant improvement in accuracy and computational efficiency. In summary, the new algorithm shows significant advantages in terms of accuracy and stability, providing a powerful tool for the analysis of athletic postures in the field of sports. Meanwhile, this research result has important application prospects in fields such as sports training, sports medicine, and virtual reality.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200163"},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538104","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 and implementation of J2EE-based statement feature recognition in English teaching system optimization","authors":"Lina Wang","doi":"10.1016/j.sasc.2024.200162","DOIUrl":"10.1016/j.sasc.2024.200162","url":null,"abstract":"<div><div>With the development of Internet technology, network English teaching system came into being and developed rapidly. Based on optimized J2EE, this paper presents the implementation of sentence feature recognition in the English teaching system. Optimize the load balancing algorithm on the basis of cloud computing technology, and improve the teaching service providing ability of online teaching system based on J2EE. The technology integration of Sturts2, Spring, and Batis was realized to realize the persistence layer, business layer, and presentation layer respectively through the three frameworks. Then, the technology of Struts2 and Spring, Spring, and Batis software is integrated to analyze and build the current popular SSI lightweight framework, and RBAC is used to provide a security mechanism for the SSI framework. It establishes that the information system should adopt the mixed architecture of B/S architecture and C/S architecture, and then design the overall functional structure of the system with students, teachers, and administrators as the main users from the perspective of users. This paper analyzes and explains the overall structure of the J2Ee-based English teaching system, briefly introduces the overall framework of the whole website, and introduces the main functions of each functional module of the website. Finally, the English teaching system based on optimized J2EE statement feature recognition is implemented and tested. In the performance test of file resource query service with virtual 10–100 users and 20 times submitted by each user, the response time of the system is &lt;1.5 s, the success rate reaches 100 %, and the CPU utilization is also &lt;5 %. The memory usage is relatively high. When 2000 queries are concurrent, the memory usage reaches &gt;160 M.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200162"},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538105","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":"Advancing sustainable mobility: Dynamic predictive modeling of charging cycles in electric vehicles using machine learning techniques and predictive application development","authors":"Biplov Paneru ,&nbsp;Durga Prasad Mainali ,&nbsp;Bishwash Paneru ,&nbsp;Sanjog Chhetri Sapkota","doi":"10.1016/j.sasc.2024.200157","DOIUrl":"10.1016/j.sasc.2024.200157","url":null,"abstract":"<div><div>The main goal in this research is to train various machine learning models to predict charging cycles in EV Electric Vehicles) battery systems. The considered models are gradient boosting, random forests, decision trees, and linear regression. Each of these was assessed based on its R-squared score, which is an important statistical measure in indicating the variance proportion yielded by the model. In contrast, the Random Forest model significantly improved, with an R-squared value of 0.83, thereby doing an excellent job in capturing nuances of the data. Only surpassed by the Gradient Boosting model at an astonishing R-squared score of 0.87, it is this excellent score that underlines its capability to predict the outcome quite accurately by modeling complex interrelations. In other words, gradient boosting outran the rest and provided the most robust results concerning drivers of students' performance. It also underlines how important choosing a good model is in educational analytics in order to increase the accuracy of the predictions. The use of these models in the proposed EV Battery Charging Cycle Predictor App results in accurate predictions to aid schedule maintenance and energy-related decisions. This research brings light to the future of advanced machine learning methods in enhancing the battery efficiencies of EVs and the development of electric mobility technologies. It is possible that the future work will imply the additional inclusion of real data and the integration of the application to general energy systems.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200157"},"PeriodicalIF":0.0,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538106","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":"Innovative application of particle swarm algorithm in the improvement of digital enterprise management efficiency","authors":"Shengnan Zhang","doi":"10.1016/j.sasc.2024.200151","DOIUrl":"10.1016/j.sasc.2024.200151","url":null,"abstract":"<div><div>At present, the management of most enterprises still adopts the traditional business model, which is difficult to meet the requirements of modern informatization. To effectively improve the efficiency of digital enterprise management and solve the limitations of traditional management methods in resource allocation, decision-making, and process optimization, an experiment is proposed for a digital enterprise innovation management method based on Particle Swarm Optimization. The research results show that the method is applied to the enterprise for simulation experiments, and the efficiency obtained after using the method is as high as 99.5 %, which is nearly 2 % higher than the enterprise management efficiency obtained before the method is not used. The results show that the proposed Particle Swarm Optimization has high reliability and accuracy for improving the management efficiency of digital enterprises, and can provide new research directions and ideas for the development and progress of enterprises in the Internet era.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200151"},"PeriodicalIF":0.0,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538107","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}