International Journal of Cognitive Computing in Engineering最新文献

{"title":"Integrating environmental clustering to enhance epidemic forecasting with machine learning models","authors":"Yosra Didi ,&nbsp;Ahlam Walha ,&nbsp;Ali Wali","doi":"10.1016/j.ijcce.2025.06.001","DOIUrl":"10.1016/j.ijcce.2025.06.001","url":null,"abstract":"<div><div>The COVID-19 pandemic underscored the urgent need for more accurate and adaptive forecasting models to support public health decision-making and limit disease spread. However, many existing models overlook the influence of environmental and climatic factors that significantly affect transmission dynamics. This study addresses this gap with a novel forecasting framework that integrates environmental data into predictive modelling. Our key contributions are threefold: (1) we analyse the relationship between environmental variables (temperature, humidity, and air quality) and COVID-19 trends across countries; (2) we propose a two-stage approach combining K-means clustering to group countries based on environmental conditions, followed by region-specific machine learning models using Support Vector Regression (SVR), Prophet, and Long Short-Term Memory (LSTM) networks for both univariate and multivariate time series forecasting; and (3) we demonstrate that LSTM significantly outperforms other models, achieving superior accuracy for 30-day COVID-19 case predictions. Our results highlight the importance of incorporating environmental variables in epidemic modelling and offer a practical tool for more targeted and effective public health responses. This research provides actionable insights that can inform the design of climate-aware forecasting systems for future pandemic preparedness.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 628-642"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501480","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":"Research on Collaborative Optimization Strategy of Railway Signal Nonlinear Control System Based on BBO Algorithm and Multi-objective Optimization","authors":"Xue Li ,&nbsp;Yixuan Yang ,&nbsp;Zheng Li ,&nbsp;Hui He","doi":"10.1016/j.ijcce.2025.05.005","DOIUrl":"10.1016/j.ijcce.2025.05.005","url":null,"abstract":"<div><div>This study focuses on exploring collaborative optimization strategies for a nonlinear control system of railway signals based on the BBO algorithm. Currently, the railway signal control system faces performance bottlenecks such as response lag and local optima due to parameter coupling when dealing with multi-objective optimization problems like train operating speed and signal delays. Traditional optimization methods struggle to achieve global collaborative regulation under complex operating conditions. Therefore, there is an urgent need to introduce efficient intelligent algorithms to enhance the system's real-time capabilities and reliability. The research constructs a mathematical model with multiple objective constraints, accurately identifies the adaptation shortcomings of the existing system in dynamic scenarios, and then employs a Biogeography-Based Optimization (BBO) algorithm for global optimization of control parameters. Specifically, it sets a population size of 50, a maximum number of iterations of 200, a migration rate dynamically adjusted between 0.6-0.9, and an adaptive mutation rate of 0.01-0.05, using root mean square error and response time as performance evaluation metrics for parameter optimization. Experimental data show that compared to traditional methods, this strategy can increase the average operating speed of trains by 15%, reduce signal delays by 20%, and improve system robustness indicators by 18.5%, achieving a collaborative enhancement of efficiency and safety while ensuring stable operation, thus providing an engineering-valued solution for the intelligent upgrade of railway transport.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 617-627"},"PeriodicalIF":0.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490782","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":"Design and implementation of classical literature sentiment analysis system based on ensemble learning and graph neural network","authors":"Qianru Gao ,&nbsp;Jiachen Huang","doi":"10.1016/j.ijcce.2025.05.004","DOIUrl":"10.1016/j.ijcce.2025.05.004","url":null,"abstract":"<div><div>Classical literary works have attracted extensive attention in modern society because of their unique cultural memory and aesthetic value. However, due to the long history and evolution of language, how to accurately grasp its connotation, especially its emotional color, has always been a dilemma for researchers. This study is committed to the design and implementation of a classical literature sentiment analysis system based on ensemble learning and graph neural network, with the goal of breaking through the limitations of traditional methods and realizing the refined analysis of classical literature sentiment tendencies. By constructing a large-scale corpus covering classics from different eras, this study lays a solid data foundation for model training. Graph neural network technology is innovatively applied to sentiment analysis in classical literature, and the graph structure composed of lexical nodes and semantic edges is used to capture the deep semantic and structural connections of texts. At the same time, bagging and boosting ensemble learning strategies are introduced to optimize the performance of multiple GNN models and form a more robust decision set. Experimental results show that compared with traditional methods, the graph neural network has an accuracy of 91.5 % for sentiment classification, and the ensemble learning further reduces the false positive rate, improving the overall emotion recognition accuracy of the system to 93.7 %, providing an efficient and accurate innovative solution for sentiment analysis of classical literature.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 603-616"},"PeriodicalIF":0.0,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482417","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":"A novel region based neighbors searching classification algorithm for big data","authors":"Rajib Kumar Halder,&nbsp;Mohammed Nasir Uddin,&nbsp;Md Ashraf Uddin","doi":"10.1016/j.ijcce.2025.05.002","DOIUrl":"10.1016/j.ijcce.2025.05.002","url":null,"abstract":"<div><div>The K-Nearest Neighbors (KNN) algorithm remains a cornerstone of machine learning due to its intuitive design and effectiveness in classification tasks. However, its performance often suffers from critical limitations, such as sensitivity to the choice of the parameter K and an inability to effectively capture complex relationships among neighboring instances. To overcome these challenges, we propose the <strong>Region-Based Neighbors Searching Classification Algorithm (RNSCA)</strong>—a novel, adaptive framework that significantly enhances the scalability, flexibility, and accuracy of traditional KNN, especially in high-dimensional and large-scale datasets. RNSCA leverages dynamic, region-based partitioning for more focused and efficient neighbor searches and incorporates a weighted activation function to prioritize the most relevant data points during classification. Additionally, ensemble learning techniques are integrated to strengthen model robustness and improve generalization. The proposed algorithm is extensively validated on benchmark datasets including Iris, Crop Recommendation, Breast Cancer, Diabetes, and Chronic Kidney Disease (CKD). Experimental results consistently demonstrate RNSCA’s superior performance in modeling nuanced local structures and mitigating the core limitations of conventional KNN. This research presents a compelling advancement in classification algorithms, with practical implications across domains such as healthcare, agriculture, and environmental intelligence.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 516-536"},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932195","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":"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":"2025-05-05","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":"An efficient and high-performance WSNs restoration algorithm for fault nodes based on FT in data aggregation scheduling","authors":"Cheng Li ,&nbsp;Guoyin Zhang","doi":"10.1016/j.ijcce.2025.05.001","DOIUrl":"10.1016/j.ijcce.2025.05.001","url":null,"abstract":"<div><div>In wireless sensor networks(WSNs), data aggregation effectively reduces network traffic, thereby reducing energy consumption and improving network life cycle. Nevertheless, in the process of data aggregation scheduling, if there are fault nodes, the data quality collected by the whole network will decline, and the network performance will decrease, even posing a threat to network security or causing network paralysis. Thus, an efficient and high-performance WSNs restoration algorithm is proposed based on fat tree(FT), which is referred to as the EPRA-FT algorithm. And our goal is to improve the universality, efficiency, and performance retention of the algorithm. Previously, we have conducted a range of the relevant researches on performance improvement of WSNs aggregation scheduling by adopting FT structure, and some successful results have been obtained. On the basis of these results, for the EPRA-FT algorithm, first and foremost, the relationship among nodes is comprehensively recorded in FT construction process. Then, fault nodes are shielded by deleting the known nodes in aggregation tree. Finally, the local reconfiguration of aggregation tree is completed quickly and efficiently. Meanwhile, the aggregation scheduling performance of the original network is maintained to the maximum extent. The feasibility and superiority of our proposed EPRA-FT algorithm are proved by simulation experiments.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 508-515"},"PeriodicalIF":0.0,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917830","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":"Development of an efficient method for object detection and localization in 3D space using RGBD cameras for autonomous systems","authors":"Nataliya Boyko","doi":"10.1016/j.ijcce.2025.04.005","DOIUrl":"10.1016/j.ijcce.2025.04.005","url":null,"abstract":"<div><div>The work presents an efficient algorithm for object detection, orientation estimation, and isometric positioning in 3D space using RGBD camera data. The goal of the study is to improve the accuracy and processing speed of autonomous navigation and manipulation systems under conditions of limited computational resources. The proposed approach combines heuristic isometry estimation with segmentation methods (DBSCAN), plane estimation (RANSAC), and orientation analysis, enabling effective processing of scenes with planar backgrounds. The main advantage of the algorithm lies in its ability to operate in real time: the processing time for a single frame is only 20 ms, achieving object positioning accuracy up to 5.48 cm. The results of experimental research confirm a high level of accuracy and stability even under challenging conditions. The algorithm outperforms existing models in terms of processing speed while demonstrating comparable or superior positioning accuracy. The practical significance of the proposed method lies in its potential application in mobile robotics, automated warehouse systems, and machine vision systems where high autonomy and precision are required. The algorithm can also be adapted to a broader range of tasks due to its flexible hyperparameter tuning. A key limitation remains the requirement for object placement on a planar surface and the use of a depth camera, which necessitates a specific environmental setup. The proposed method makes a significant contribution to the advancement of computer vision and autonomous robotics technologies, opening prospects for its implementation in next-generation systems.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 537-551"},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942054","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":"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":"2025-04-26","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":"Enhancing elderly care services through integrated sentiment analysis and knowledge reasoning: A deep learning approach","authors":"Yongguan Ai ,&nbsp;Shiwei Chu ,&nbsp;Juan Wang ,&nbsp;Nianfang Xu","doi":"10.1016/j.ijcce.2025.04.003","DOIUrl":"10.1016/j.ijcce.2025.04.003","url":null,"abstract":"<div><div>This study proposes a pioneering integrated care model for elderly care service robots that integrates sentiment analysis and knowledge reasoning through a deep learning framework. The primary objective of this research is to address the limitations of current elderly care robots in providing emotionally intelligent and personalized care. The model utilizes advanced deep learning techniques, such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), to analyze multimodal data comprising speech, facial expressions and body language. This enables the model to provide a comprehensive understanding of an elderly individual's emotional and health status. The efficacy of the model is demonstrated by its ability to enhance the precision of care decisions, improve the quality of care, user satisfaction, and system reliability. The experimental results demonstrate substantial improvements in sentiment recognition accuracy (96.5 %), reasoning accuracy (93.7 %), decision execution time (3.2 s), user satisfaction (4.9 points), and system stability (98.4 %), highlighting the transformative potential of the model in revolutionizing elderly care services.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 477-494"},"PeriodicalIF":0.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842158","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":"Exploring insights on deep learning-based photovoltaic fault detection for monofacial and bifacial modules using thermography","authors":"Eko Adhi Setiawan ,&nbsp;Muhammad Fathurrahman","doi":"10.1016/j.ijcce.2025.04.001","DOIUrl":"10.1016/j.ijcce.2025.04.001","url":null,"abstract":"<div><div>Routine maintenance of photovoltaic (PV) power plants is critical to mitigate module faults, which can result from environmental factors, reducing power output and accelerating module degradation. To effectively detect faults across the entire PV module array, aerial infrared thermography (AIRT) is employed, using unmanned aerial vehicles (UAVs) to capture thermal images via predetermined waypoints. Afterward, these images are analyzed by a deep learning (DL) model known for its objec detection accuracy, identifying modules requiring further inspection. While prior research has focused on monofacial modules, limited studies have examined bifacial modules, which are rapidly gaining market share due to their albedo characteristics that increase energy yields in high-albedo areas. Thus, research on bifacial performance and faults is essential to support the development of PV maintenance systems across diverse environments. This study tests bifacial modules under PV fault conditions using thermography, adhering to established inspection standards, which enables comparative analysis with monofacial modules. Furthermore, our PV fault detection model uses a novel dataset from thermal images of both module types to train a mask region-based convolutional neural network (Mask R-CNN). The experiment demonstrated that, under similar irradiation conditions, bifacial faults exhibit higher temperatures and show distinct surface patterns in their thermal images. Despite these variations, our model detected PV faults in both module types, achieving a mean average precision (mAP) of 84.27 %. The model's performance could be further enhanced by expanding the bifacial dataset to address challenges in detecting soiling defects, which vary in shape, size, and location.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 495-507"},"PeriodicalIF":0.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143890669","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}