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":"2025-12-01","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":"Enhancing agricultural research with an Attention-Based Hybrid Model for precise classification of rice varieties","authors":"Nuzhat Noor Islam Prova","doi":"10.1016/j.ijcce.2025.02.002","DOIUrl":"10.1016/j.ijcce.2025.02.002","url":null,"abstract":"<div><div>As a staple food feeding over half of the world’s population, rice needs well defined classification techniques to improve agricultural yields, the supply chain, and food safety. To meet these needs, this work presents an Attention-Based Hybrid Model that can accurately and effectively classify Bangladeshi rice varieties. This research covers complex variations of feature space in shape, texture, and color based on 20 different rice varieties by using a comprehensive dataset of 27,000 high-resolution images that include real-world agricultural issues. The core innovation is proposed based on the Attention-Based CNN and CBAM structure. This in fact effectively highlights and enhances spatially and channel-orientated features, allowing the model to tell apart morphologically similar types of rice with high accuracy. The proposed Attention-Based CNN had achieved an accuracy of 91.92%, which leads to an improvement in both generalization aspects and robustness across different categories of testing conditions. Moreover, extending the proposed framework, feature extraction combined with KNN classifier reported the top accuracy of 99.35% proving that modern feature extraction and classification algorithms go hand in hand. This new combined approach does better than Random Forest and Support Vector Classifier because it solves problems that are normally associated with it such as finegrained features and scaling. Apart from that, the model represents one level up from the current paradigm of automated agriculture, bringing a robust, standardized, and flexible solution for rice variety identification. That way, this study provides a way of connecting a technological standpoint with the requirements that farmers have to address in order to further the issue of sustainability, support precision agriculture, and address the growing need for food quality and security in the world.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 412-430"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508701","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":"AI-based model for securing cognitive IoT devices in advance communication systems","authors":"Akshat Gaurav ,&nbsp;Varsha Arya ,&nbsp;Kwok Tai Chui ,&nbsp;Brij B. Gupta","doi":"10.1016/j.ijcce.2025.01.009","DOIUrl":"10.1016/j.ijcce.2025.01.009","url":null,"abstract":"<div><div>The pervasive integration of the Cognitive Internet of Things (CIoT) in advanced communication systems has significantly heightened security concerns due to inherent vulnerabilities that cyber attackers exploit. These security challenges are exacerbated by IoT devices’ limited computational resources and storage capacities, rendering traditional security protocols ineffective. This paper introduces a AI- based security model tailored explicitly for IoT environments within advanced communication systems. Our approach leverages AI techniques for cyber attack detection and mitigation. The effectiveness of our model is demonstrated through its capability to identify different types of cyberattacks with a precision of more than 99%. This study advances the security mechanisms for IoT devices and offers a scalable and efficient solution to safeguard user privacy and system integrity in sophisticated communication networks.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 351-359"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139945","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":"Emotional analysis of broadcasting and hosting speech by integrating grid PSO-SVR and PAD models","authors":"Shasha Pan","doi":"10.1016/j.ijcce.2024.11.002","DOIUrl":"10.1016/j.ijcce.2024.11.002","url":null,"abstract":"<div><div>Traditional speech emotion analysis techniques are influenced by factors such as environment and speech speed. Given this, this study assumes the combination of grid particle swarm optimization algorithm and support vector regression, combined with a pleasure-arousal-dominance emotion dimension model, to improve the accuracy of speech emotion analysis. This paper first constructs a speech emotion model that combines the pleasure-arousal-dominance dimensions of particle swarm optimization and support vector regression. Then, a comparative analysis is conducted on the performance of the sentiment analysis model in this dimension and its applicability in the direction of broadcasting hosting. In the experiment of standard deviation and average value of pleasure-arousal-dominance dimension, the scoring average curve of the dimension of the research algorithm was almost a straight line, and the components of pleasure-arousal-dominance were normally distributed. The minimum standard deviation scores of pleasures, dominance, and arousal were 0.40, 0.56, and 0.82. The results demonstrate that the model exhibits high reliability in all dimensions of pleasure-arousal-dominance prediction, thereby substantiating its potential as a pivotal tool in the analysis of speech emotions exhibited by broadcast hosts.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 55-64"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706057","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 news sentiment classification and recommendation system based on multi-model fusion and text similarity","authors":"Qinchen Xi ,&nbsp;Peiyun Jiang","doi":"10.1016/j.ijcce.2024.11.003","DOIUrl":"10.1016/j.ijcce.2024.11.003","url":null,"abstract":"<div><div>To improve the performance of the news recommendation system, the study employs a multi-model fusion strategy in conjunction with three classification methods to improve the accuracy of sentiment classification. In addition, a text similarity-based recommendation algorithm is proposed to provide personalized recommendations to users. This is achieved by analyzing their interest features and key text features. In addition, a comprehensive recommendation system including news sentiment classification, recommendation algorithms, and a user interface is developed. The study showed that the sentiment classification method based on multi-model fusion had an accuracy rate of 0.95, which was an improvement of 0.10 compared to the support vector machine. Furthermore, the recommendation algorithm based on text similarity calculation demonstrated an accuracy, recall, and F1 value of 0.806, 0.804, and 0.806, respectively, which outperformed the collaborative filtering algorithm and the matrix decomposition recommendation algorithm. The average satisfaction rating for the news recommendation content was above 90 points, indicating that it met user needs. The findings indicate that consumers perceive the personalized news suggestion system to be well-received and that it functions effectively in terms of reaction time and resource consumption. This research offers an effective solution for news recommendation in the era of information overload, which holds significant theoretical and practical value.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 44-54"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706058","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":"Sentiment analysis method of consumer reviews based on multi-modal feature mining","authors":"Jing You ,&nbsp;Jiamin Zhong ,&nbsp;Jing Kong ,&nbsp;Lihua Peng","doi":"10.1016/j.ijcce.2024.12.001","DOIUrl":"10.1016/j.ijcce.2024.12.001","url":null,"abstract":"<div><div>Traditional sentiment analysis methods primarily rely on textual data. However, in real-world applications, product reviews often contain multimodal information including images, videos, and audio. This multimodal data is crucial for accurately understanding consumer sentiment trends. Therefore, this article proposes a novel product review sentiment analysis method that leverages multimodal feature mining. Firstly, a pre-trained vision transformer (VIT) and baidu general embedding (BGE) models are utilized to encode image and text features. Then, internal correlation mining is performed on image and text features through cross-attention. Next, the image and text features are aligned using a mask graph transformer network. The final encoding is obtained using multi-layer perceptron. Lastly, cosine similarity is computed between this encoding and each sentiment aspect of BGE encoding to determine corresponding sentiment scores. Simulations were conducted on a multi-modal Multi-ZOL dataset to compare the proposed method with several state-of-the-art techniques. The obtained results validated the superior performance of the proposed method.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 143-151"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139987","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 interactive address matching method based on a graph attention mechanism","authors":"Ming Li,&nbsp;Jialin Su,&nbsp;Zhiyu Song,&nbsp;Juping Qiu,&nbsp;Yongping Lin","doi":"10.1016/j.ijcce.2024.12.003","DOIUrl":"10.1016/j.ijcce.2024.12.003","url":null,"abstract":"<div><h3>Problem:</h3><div>Modernizing and standardizing place names and addresses is a key challenge in the development of smart cities.</div></div><div><h3>Purpose:</h3><div>This paper proposes a solution to address matching challenges, such as incomplete descriptions, reversed word order, and the diverse descriptions often found in Chinese addresses.</div></div><div><h3>Method:</h3><div>Leveraging the hierarchical structure of Chinese addresses, this study introduces the interactive address matching graph attention model (IAMGAM). In the IAMGAM, an attention-based feature interaction method (AFIM) is employed. To reflect the hierarchical nature of address elements, a directed graph is used to model the address data, and the model is trained and tested using a graph attention mechanism.</div></div><div><h3>Results:</h3><div>Experiments demonstrate that the IAMGAM achieves an accuracy and F1-score of 99.61%. Compared with the existing address matching methods, the IAMGAM improves the accuracy by 0.66% to 2.57%, and the F1-score by 0.68% to 2.55%, outperforming baseline models. Additionally, ablation experiments confirm the effectiveness of each component within the model. Furthermore, when fine-tuned using ChatGLM2-6B, the results show that the IAMGAM still outperforms ChatGLM2-6B.</div></div><div><h3>Conclusion:</h3><div>IAMGAM demonstrates excellent performance in Chinese address matching tasks, and the Large Language Model (LLM)-based methods, such as ChatGLM2-6B, show great potential for future development in this area.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 191-200"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139988","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":"Tennis teaching assistance model based on double chain shared unsupervised action recognition algorithm","authors":"Hongzhou Bai","doi":"10.1016/j.ijcce.2024.10.001","DOIUrl":"10.1016/j.ijcce.2024.10.001","url":null,"abstract":"<div><div>The traditional self-supervised methods based on skeleton data frequently categorize the various enhancements of a given sample as positive examples, while the remaining samples are designated as negative examples. This approach results in a significant imbalance in the ratio of positive to negative samples, which in turn constrains the efficacy of samples with identical semantic information. Therefore, to further improve the training quality in tennis teaching and enhance the accuracy of action recognition, a dual chain sharing unsupervised action recognition algorithm has been proposed. This study first designs a skeleton topology data augmentation method based on the physical connections of human joints to obtain advanced semantic embeddings. Then, an improved positive sample expansion strategy is utilized to enhance the diversity and quality of training data. Next, an unsupervised learning mechanism is employed to autonomously learn and recognize complex patterns of tennis movements. To measure the performance of the model, tests were conducted on its accuracy, F1 score, recognition time, and fitting degree. The experimental results showed that the proposed algorithm could reach its optimal state after 23 iterations of training, and its F1 value reached 0.918, with an average accuracy of 92.9 % and an average recognition time of 7.4 s. The research algorithms were superior to the multi-input branch graph convolutional network action recognition algorithms used for comparison, pull-push contrastive loss action recognition algorithms, and multi-granularity anchor contrastive representation learning action recognition algorithms. Its accuracy was leading by 8.6 %-23.5 %, and the recognition time has been reduced by 2.3s-7.4 s. In addition, in terms of the fitting degree of action recognition, compared with other methods, the proposed method had a fitting degree of up to 95.7 %, which was at least 8.1 % higher. This research method can improve the timeliness of feedback from trainers by accurately and quickly recognizing movements, helping coaches develop more targeted training plans, thereby improving the efficiency and quality of tennis teaching.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 21-31"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539780","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":"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":"2025-12-01","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":"Examining finite-time behaviors in the fractional Gray–Scott model: Stability, synchronization, and simulation analysis","authors":"Shaher Momani ,&nbsp;Iqbal M. Batiha ,&nbsp;Issam Bendib ,&nbsp;Adel Ouannas ,&nbsp;Amel Hioual ,&nbsp;Dalah Mohamed","doi":"10.1016/j.ijcce.2025.02.004","DOIUrl":"10.1016/j.ijcce.2025.02.004","url":null,"abstract":"<div><div>This paper investigates the behavior and stability of the fractional-order Gray–Scott model, with a specific focus on achieving finite-time stability and synchronization. It introduces essential concepts, including the Gamma function, the Riemann–Liouville fractional-order integral operator, the Caputo fractional derivative, and the Mittag-Leffler function, to establish a foundational framework for subsequent analysis. Equilibrium points are defined, distinguishing between initial and finite-time equilibria, and the conditions for finite-time stability, including settling time, are precisely outlined. Stability results for this model are presented through theorems with detailed proofs, elucidating the roles of Lyapunov functions, class functions, and other system parameters. Furthermore, the paper explores finite-time synchronization schemes in master–slave systems, providing a mathematical framework for understanding and achieving synchronization within a finite time frame. This framework illuminates synchronization dynamics and their practical implications for controlling complex systems. Additionally, numerical examples illustrate finite-time stability and synchronization within the Gray–Scott reaction–diffusion model.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 380-390"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445698","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}