International Journal of Cognitive Computing in Engineering最新文献

{"title":"Normalized SPSA for Hammerstein model identification of twin rotor and electro-mechanical positioning systems","authors":"Nik Mohd Zaitul Akmal Mustapha,&nbsp;Mohd Ashraf Ahmad","doi":"10.1016/j.ijcce.2025.04.004","DOIUrl":"10.1016/j.ijcce.2025.04.004","url":null,"abstract":"<div><div>A wide range of optimization methodologies have been introduced for identifying Hammerstein model systems, but existing approaches often face challenges such as convergence instability, computational inefficiency, and over-parameterization. These issues necessitate research into fast, stable, and precise identification methods. This study proposes the normalized simultaneous perturbation stochastic approximation (N-SPSA) to address the challenges mentioned earlier. The N-SPSA mitigates unstable convergence and excessive parameter growth of the conventional SPSA by normalizing objective functions to their highest value, ensuring stable convergence while maintaining the same number of coefficients. The effectiveness of the proposed method was validated by modeling the actual systems, which included the twin-rotor system (TRS) and the electro-mechanical positioning system (EMPS). Performance metrics such as the objective functions statistics, the number of function evaluations (NFE), and time- and frequency-domain responses were used for evaluation. For the TRS, the N-SPSA improved the mean objective function by 18.09 % compared to the average multi-verse optimizer sine-cosine algorithm (AMVO-SCA) and 3.42 % compared to the norm-limited (NL-SPSA), while reducing the computational load by 60 % compared to the AMVO-SCA. Similarly, for the EMPS, the N-SPSA improved the mean objective function by 71.19 % over the NL-SPSA and 25.18 % over the AMVO-SCA, achieving a 50 % reduction in computational effort compared to the AMVO-SCA. Additionally, Wilcoxon’s rank-sum test results for both the TRS and EMPS confirmed the statistical superiority of the N-SPSA over the NL-SPSA. These findings demonstrate that the N-SPSA provides a fast and precise solution for the identification of continuous-time Hammerstein systems, overcoming the limitations of existing methods.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 552-568"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The application of GCN algorithm in Building Construction Knowledge Graph updating under the combination of artificial intelligence and knowledge management","authors":"Lu He,&nbsp;Xu Hu","doi":"10.1016/j.ijcce.2024.11.001","DOIUrl":"10.1016/j.ijcce.2024.11.001","url":null,"abstract":"<div><div>To promote the updating and iteration of the construction field, the construction knowledge graph can expand the professional knowledge system within the field and provide scientific and reasonable management decisions. The research adopts deep learning technology to update the knowledge graph in computer vision. The specific update method is to extract entities and analyze entity relationships in the framework of the knowledge graph, to achieve the application of the knowledge graph in the field of construction. Afterwards, a graph convolutional neural network is used to integrate semantic information and construct a network model of semantic embedding vectors. Finally, zero sample learning technologies are combined to enhance prior information during the construction process, thus demonstrating a better backward process for updating iterations. Through testing and validation of the dataset, it is found that the highest TOP-1 accuracy of the zero sample object detection model is 77 %, 30 %, and 45 %, respectively, under different proportions of the dataset. The TOP-1 accuracy of the zero sample action recognition model increases by 22 %, 23 %, and 41 %, respectively, compared to the former. In addition, the detection model combined with semantic embedding vectors has an average accuracy of 45 % for unknown class tasks at a ratio of 8:2. This proves that the performance of the zero sample human object interaction detection model based on semantic embedding vectors is superior, providing a knowledge foundation and theoretical support for the updating of knowledge graphs in the field of construction.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 65-73"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743963","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 network differentiation service method using DA-VNE algorithm","authors":"Lei Li","doi":"10.1016/j.ijcce.2025.03.001","DOIUrl":"10.1016/j.ijcce.2025.03.001","url":null,"abstract":"<div><div>To address the issue of limited network resources being unable to provide efficient differentiated service, a virtual network mapping algorithm based on delay awareness has been proposed, along with an intelligent network traffic classification and adjustment architecture. By paying attention to delay awareness, the low-latency requirements of different applications are realized, and a framework that can provide customized services according to different types of traffic is designed. The experimental results demonstrated that the proposed virtual mapping algorithm reduced node average latency by 23.61 % and 34.28 %, respectively, compared to the other two algorithms. Furthermore, it reduced link average latency by 35.01 % and 41.82 %, respectively. The success rate of this algorithm in 20 virtual network requests was 83.59 %, which was 3.17 % and 26.65 % higher than the other two algorithms. The results indicated that the delay-aware cross-domain virtual network mapping algorithm had significant advantages in the nodes, links, and overall latency of virtual network requests. The network-differentiated service method based on this algorithm can greatly satisfy the latency demands of different applications and alleviate network traffic congestion. The designed network architecture is feasible and reasonable in network traffic scheduling.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 451-461"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682353","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 clearness processing for image restoration based on generative adversarial networks","authors":"Xin Wang ,&nbsp;Na E ,&nbsp;Jingbo Yang","doi":"10.1016/j.ijcce.2025.01.005","DOIUrl":"10.1016/j.ijcce.2025.01.005","url":null,"abstract":"<div><div>Image restoration and enhancement techniques have found widespread applications in the field of digital image processing, spanning areas such as medical imaging, remote sensing image analysis, and historical document restoration. However, with the advancements in generative adversarial networks, existing technologies are no longer sufficient to meet the high-definition requirements of image restoration. In light of this, the experiment proposes an image restoration and enhancement technique based on generative adversarial networks and style perception. The proposed model synergistically combines the image feature capturing ability of generative adversarial networks with the feature fusion capability of a multi-scale attention mechanism. This aims to address the complexities and information loss encountered in traditional image restoration processes. The results indicated that the proposed method, when applied to the Helen Face dataset, consistently increased the average Peak Signal-to-Noise Ratio (PSNR) values across four models. At a system runtime of 0.387 s, the PSNR value for the proposed method reached 52.84 dB, while the PSNR values for the other algorithms continue to increase. On the CelebA dataset, when the proposed method achieved the maximum Structural Similarity Index (SSIM) value, the corresponding number of restored images was 360, with a continuously rising SSIM value reaching 0.968. A comparative analysis of the four methods for image restoration revealed that the proposed method exhibited the highest degree of consistency with real images, demonstrating superior performance in handling details that closely resemble those in real images. The restoration effectiveness of the proposed method surpassed the other three methods significantly. These results indicate that the proposed method yields the best results for image restoration. Furthermore, the stability and low error of the system operation greatly enhance image recognition accuracy, effectively resolving issues related to identity coherence during the image restoration process.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 360-369"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387808","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":"An efficient hybrid Hopfield convolutional neural network for detecting spam bots in Twitter platform","authors":"A.V. Santhosh Kumar ,&nbsp;N. Suresh Kumar ,&nbsp;R. Kanniga Devi","doi":"10.1016/j.ijcce.2025.05.003","DOIUrl":"10.1016/j.ijcce.2025.05.003","url":null,"abstract":"<div><div>Recently, social media platforms have become very popular as they offer unbelievable opportunities to their users. Twitter is one of the social media platforms on which a huge number of people exchange their messages by posting tweets. However, this platform is usually used by automated accounts called bots. Such bots are used to spread fake news, fake ideas, and products. Hence, it is essential to detect the presence of spam bots on Twitter. In order to detect spam bots on Twitter, an effective feature selection technique using a novel hybrid deep learning model is introduced in this paper. This paper proposes a novel spam bot detection system for the Twitter social network that combines profile and tweet-based features. Initially, the Twitter data are pre-processed to improve the accuracy of classification. The pre-processing stage involves various steps such as stopping word removal, tokenization, stemming, n-gram identification, user mention, and vocabulary density and richness. After pre-processing, the tweets are given to the next stage for feature extraction. In this stage, the user profile-based features such as name, screen name, location, and time, as well as the tweet-based features such as hashtags, retweeting of tweets, etc., are extracted from the tweets. The extracted features are then subjected to feature selection, where a meta-heuristic-based optimization algorithm called the Binary Golden Search Optimization algorithm (BGSO) is used. This method helps to reduce the feature dimensionality and overfitting issues. In order to improve the optimization algorithm’s searching ability, an X-shaped transfer function is used. Finally, the selected features are provided to the novel Hybrid Hopfield Dilated Depthwise Separable Convolutional Neural Network (HHD²SCNN) based classification model, where the output layer classifies the given tweets as spam bots or legitimate. The proposed method is experimentally verified, and the performance metrics are evaluated. Simulation is done using the Python tool, and the Cresci 2017 dataset is used. Simulation results show that the proposed HHD²SCNN model provides better accuracy, having an accuracy of 98.40 % compared to the existing techniques. Also, the proposed hybrid deep learning model achieved a precision of 98.40 %, recall of 98.40 %, specificity of 98.40 %, F-score of 98.40 %, and kappa of 96.80 %. Thus, the proposed technique achieves better results compared to the existing techniques.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 569-587"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart real-time detection of risky roads using vehicles trajectories for intelligent transportation","authors":"Eman O. Eldawy ,&nbsp;Mohammed Abdalla ,&nbsp;Hoda M.O. Mokhtar ,&nbsp;Abdeltawab Hendawi ,&nbsp;Amr M. AbdelAziz","doi":"10.1016/j.ijcce.2025.01.001","DOIUrl":"10.1016/j.ijcce.2025.01.001","url":null,"abstract":"<div><div>Indeed, risky roads have a negative impact on traffic by causing road injuries with fatalities, which can lead to negative emotional, social, and economic influences on humans, countries, and the world. Additionally, taxi and rideshare passengers prefer to move on familiar and safe roads. Therefore, to ensure the high quality of transportation services, it is required to follow secure roads to avoid poorly maintained roads, areas with high incidences of car accidents, neighborhoods with high crime rates, and places with a history of terrorist attacks or civil unrest. In this regard, discovering risky roads is a need. This paper introduces a real-time framework, named <em>RiskyMove</em>, that helps drivers and passengers to follow safe roads and avoid risky once that are not safe for travel. Mainly, the <em>RiskyMove</em> framework employs a probabilistic method based on a Minimum Adaptive Viterbi (MAV) algorithm to identify risky paths during the trip and alarm the drivers to take precautions. An experimental evaluation of the <em>RiskyMove</em> with a real dataset of the movement of cabs in San Francisco illustrates the effectiveness of the proposed framework.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 370-379"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of the vision-based deep learning technique for waste classification using the robotic manipulation system","authors":"Huu Tran Nhat Le ,&nbsp;Ha Quang Thinh Ngo","doi":"10.1016/j.ijcce.2025.02.005","DOIUrl":"10.1016/j.ijcce.2025.02.005","url":null,"abstract":"<div><div>To maintain a green society, efficient waste management is crucial. Traditional manual trash sorting presents several challenges, including inaccuracies in classification and potential health risks for workers. To address these issues, this paper proposes an intelligent and automated waste classification system that integrates deep learning with robotic kinematic control. Our approach significantly improves classification accuracy, speed, and reliability compared to manual sorting. A diverse dataset containing various waste objects, including durian peels, was collected and labelled by experts. Using deep learning, the system was trained to recognize and classify objects with high precision. A camera mounted on the end-effector of robot identifies the position and orientation of object, enabling the robot to precisely pick up and sort waste items. The key advancements of our approach include (i) development of a robotic waste classification platform that enhances sorting efficiency and reduces human involvement, (ii) implementation of a model-based learning approach that achieves rapid and accurate object detection, (iii) validation through real-world experiments, demonstrating the feasibility and effectiveness of the system in complex environments. Experimental results confirm that the proposed system significantly enhances waste classification accuracy and efficiency, paving the way for safer and more intelligent waste management in smart manufacturing and environmental sustainability applications.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 391-400"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454066","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":"Leveraging cognitive computing for advanced behavioral and emotional data insights","authors":"Chunhua Du ,&nbsp;Wei Wang","doi":"10.1016/j.ijcce.2024.12.005","DOIUrl":"10.1016/j.ijcce.2024.12.005","url":null,"abstract":"<div><div>The rapid advancement of cognitive computing has reshaped the landscape of behavioral and emotional data analysis across multiple fields. In the area of student mental health research, cognitive computing emerges as a pivotal tool, enabling deeper insights into complex behavioral and emotional patterns. This paper explores the application of cognitive computing on behavioral-emotional data analysis, detailing its role, transformative benefits, and inherent challenges in addressing mental health issues among students. Through comprehensive data analysis, we identify the primary factors driving current mental health concerns in student populations and evaluate the practical applications of cognitive computing in mitigating these issues. Furthermore, this paper provides an in-depth examination of how cognitive computing can drive innovative, data-driven solutions to enhance our understanding and management of behavioral and emotional health, positioning it as a crucial technology for proactive mental health support in educational settings.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 183-190"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139983","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":"Text clustering with large language model embeddings","authors":"Alina Petukhova ,&nbsp;João P. Matos-Carvalho ,&nbsp;Nuno Fachada","doi":"10.1016/j.ijcce.2024.11.004","DOIUrl":"10.1016/j.ijcce.2024.11.004","url":null,"abstract":"<div><div>Text clustering is an important method for organising the increasing volume of digital content, aiding in the structuring and discovery of hidden patterns in uncategorised data. The effectiveness of text clustering largely depends on the selection of textual embeddings and clustering algorithms. This study argues that recent advancements in large language models (LLMs) have the potential to enhance this task. The research investigates how different textual embeddings, particularly those utilised in LLMs, and various clustering algorithms influence the clustering of text datasets. A series of experiments were conducted to evaluate the impact of embeddings on clustering results, the role of dimensionality reduction through summarisation, and the adjustment of model size. The findings indicate that LLM embeddings are superior at capturing subtleties in structured language. OpenAI’s GPT-3.5 Turbo model yields better results in three out of five clustering metrics across most tested datasets. Most LLM embeddings show improvements in cluster purity and provide a more informative silhouette score, reflecting a refined structural understanding of text data compared to traditional methods. Among the more lightweight models, BERT demonstrates leading performance. Additionally, it was observed that increasing model dimensionality and employing summarisation techniques do not consistently enhance clustering efficiency, suggesting that these strategies require careful consideration for practical application. These results highlight a complex balance between the need for refined text representation and computational feasibility in text clustering applications. This study extends traditional text clustering frameworks by integrating embeddings from LLMs, offering improved methodologies and suggesting new avenues for future research in various types of textual analysis.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 100-108"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139922","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":"Optimal path planning based on ACO in intelligent transportation","authors":"Wenyan Zhu ,&nbsp;Wenzheng Cai ,&nbsp;Hoiio Kong","doi":"10.1016/j.ijcce.2025.02.006","DOIUrl":"10.1016/j.ijcce.2025.02.006","url":null,"abstract":"<div><div>In the current intelligent transportation system, traffic congestion has become increasingly prominent. There is an urgent need for efficient path planning algorithms to solve this problem. The research aims to explore the optimal path planning scheme for intelligent transportation systems, improve traffic efficiency, shorten vehicle travel time, and allocate traffic resources reasonably. The study adopts an innovative approach that combines the global search capability of genetic algorithms with the local search advantage of ant colony algorithms. Simultaneously, the reward and punishment strategy is introduced, forming a new algorithm. The results show that the algorithm performs well in iteration time, path length, and convergence stability. Compared with traditional ant colony algorithm and genetic algorithm, the new algorithm reduces the iteration time from 45 s and 116 s to 34 s and the path length from 15,940 and 15,758 to 14,578 in the optimal path planning of 16 city coordinates. In actual distribution path planning, the optimal path length is reduced from 109.6 km to 99.2 km, and the number of iterations is reduced from 49 to 36. The research has confirmed that this algorithm effectively overcomes the slow convergence speed and susceptibility to local optima in traditional ant colony algorithms, significantly improving the accuracy and computational efficiency of path planning. It is of great significance for optimizing traffic flow management and reducing resource consumption, providing an efficient and accurate solution for path planning in intelligent transportation systems.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 441-450"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619130","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}