Systems and Soft Computing最新文献

{"title":"Cascaded intrusion detection system using machine learning","authors":"Md. Khabir Uddin Ahamed ,&nbsp;Abdul Karim","doi":"10.1016/j.sasc.2024.200182","DOIUrl":"10.1016/j.sasc.2024.200182","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200182"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148531","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":"Comprehensive material painting feature recognition based on spatial model","authors":"Jing Zhao ,&nbsp;Aiqin Liu","doi":"10.1016/j.sasc.2024.200181","DOIUrl":"10.1016/j.sasc.2024.200181","url":null,"abstract":"<div><div>Comprehensive material painting is an art form that uses multiple materials and techniques for creation. It combines traditional painting media with non-traditional materials, and this art form has become increasingly common in the field of contemporary art. However, due to the diversity and complexity of comprehensive material painting, traditional visual feature extraction methods are difficult to accurately identify and classify it. To address the above issues, a discriminative color space model is used to operate on the red green blue space, followed by standard processing, and finally Gabor wavelet analysis is performed on each subspace of the red green blue. The experimental results indicated that the model performed well in identification accuracy, recall, and F1 scores. Specifically, the identification accuracy of CMP-FEM reached 95.6 %, which was significantly higher than other contrast models such as IFE-MPA (85.00 %) and CR-GWFE (87.50 %). In addition, the application of the model in the field of painting restoration also showed its strong guiding ability, and the quality of the restored image was significantly improved. According to the comprehensive expert evaluation, the accuracy of the information identification was as high as 95.8 points, and the average F1 score of the repair guidance was 92.7 points, which further confirmed the practicality and accuracy of the model. These results demonstrate the superiority of the comprehensive material painting feature recognition model and provide an effective solution for the identification problem of painting authors.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200181"},"PeriodicalIF":0.0,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148532","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":"Intelligent pattern design using 3D modelling technology for urban sculpture designing","authors":"Wei Wan","doi":"10.1016/j.sasc.2024.200176","DOIUrl":"10.1016/j.sasc.2024.200176","url":null,"abstract":"<div><div>3D modeling is actuality hired more and more by cities to improve urban planning and cultural protection. Sculptures in settlements are the main goal of this investigate into a novel 3D-Sculpture Architecture Estimation (3D-SAE) model. This model exploits Generative Adversarial Networks (GANs) to improve images, CNNs to extract features, and LDDNN<img>HGS-ROA, a Novel Lightweight Deep Neural Network mutual with the Hunger Games Search and Remora Optimization Method, to categorize images. The GAN-based image development module reestablishes incapacitated or low-resolution sculpture photos, and the pre-trained CNN usages transfer learning to retrieve thorough features. The LDNN, tuned via HGS and ROA, brands sculpture image classification together effective and precise. This innovative method not only improves the precision of 3D reconstruction, but it also proposals a general tool for art conservationists, urban planners, and the general public in sympathetic and taking in urban sculptures. Participating these cutting-edge tools delivers a solid basis for investigating and interpreting public art, which potentials to improve cultural asset management, art conservation, and urban planning.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200176"},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148534","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":"Structural detection of goaf based on three-dimensional ERT technology","authors":"Nan Jia","doi":"10.1016/j.sasc.2024.200179","DOIUrl":"10.1016/j.sasc.2024.200179","url":null,"abstract":"<div><div>Goaf, as an underground space formed after mining, the accurate detection of its structure is crucial to mine safety and the stability of underground engineering. Although traditional detection methods, such as drilling and seismic methods, can provide certain information, they have limitations in terms of accuracy and economy. Therefore, this study used three-dimensional electrical resistivity tomography technology to more accurately detect the structure of the goaf due to its high resolution and non-invasive characteristics. At start, the development mechanism of the goaf was analyzed, and then the resistivity three-dimensional tomography technology was used to detect the goaf in the selected area through numerical simulation. The results show that when the surface deformation degree reaches 1.38%, the corresponding error of electrical resistivity tomography technology detection is 1.74%. When the surface deformation degree is 0.58% and 1.36% respectively, the corresponding errors of Multi-physics field monitoring method and the downhole transient electromagnetic method are 1.97% and 1.84% respectively. In the comparison of false negative rate, when the detection area reaches 76.8% of the regional detection area, electrical resistivity tomography technology has the lowest false negative rate, with a value of 2.412%. The accuracy of different methods was tested in the Jinggong and Open-pit areas. When the detection time was 0.51 s and 0.23 s respectively, the ERT method had the highest detection rate, with values approaching 98.57% and 100.00% respectively. During the whole process, the accuracy of the DTEM method was 87.85% and 99.99% respectively, which was much lower than that of the ERT method. An analysis of the low-resistivity anomaly areas in the selected study area found that the distribution of the observed areas showed uneven continuity, and its resistivity was low and significantly different from the surrounding rock formations. The above results illustrate that the main advantage of 3D ERT technology is its ability to provide real-time, high-density resistivity data, thereby enabling precise capture of subtle structural changes in the goaf. Compared with traditional methods, 3D ERT not only reduces environmental interference, but also significantly improves the efficiency of data collection and the accuracy of analysis, providing a new technical means for mine safety management and underground engineering.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200179"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148533","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":"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}