International Journal of Cognitive Computing in Engineering最新文献

{"title":"Dynamic Agricultural Pest Classification Using Enhanced SAO-CNN and Swarm Intelligence Optimization for UAVs","authors":"Shiwei Chu,&nbsp;Wenxia Bao","doi":"10.1016/j.ijcce.2025.04.002","DOIUrl":"10.1016/j.ijcce.2025.04.002","url":null,"abstract":"<div><div>The rapid advancement of agricultural modernization demands urgent solutions for accurate and real-time pest monitoring to enhance crop productivity. Traditional manual methods lack efficiency and fail to capture dynamic pest behaviors, while existing deep learning models struggle with robustness in complex environments. To address these challenges, this study proposes a novel Dynamic Agricultural Pest Classification System that integrates an enhanced Self-Activation Optimization Convolutional Neural Network (SAO-CNN) with bio-inspired swarm intelligence for UAVs. The SAO-CNN innovatively combines adaptive convolutional layers, self-supervised learning, and ConvLSTM to optimize spatial-temporal feature extraction, while swarm algorithms (ACO and PSO) enhance UAV path planning and task allocation. Key contributions include: (1) A hybrid SAO-CNN architecture that dynamically adjusts convolution kernels and leverages unlabeled data through self-supervised learning, improving adaptability to lighting and background variations. (2) A UAV swarm intelligence framework optimized via bio-inspired algorithms, reducing flight time by 29.2% and energy consumption by 32% compared to non-optimized systems. (3) Superior performance with 91.2% classification accuracy, 0.89 recall, and 32 FPS processing speed, outperforming state-of-the-art models (e.g., YOLO variants, ResNet, and ConvLSTM) in both static and dynamic scenarios. This work provides a robust solution for real-time pest monitoring, significantly advancing precision agriculture and sustainable crop management.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 588-602"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290809","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 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":"2025-03-12","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":"Adaptive traffic prediction model using Graph Neural Networks optimized by reinforcement learning","authors":"Mohammed Khairy ,&nbsp;Hoda M.O. Mokhtar ,&nbsp;Mohammed Abdalla","doi":"10.1016/j.ijcce.2025.02.001","DOIUrl":"10.1016/j.ijcce.2025.02.001","url":null,"abstract":"<div><div>Traffic prediction is critical for urban planning and transportation management, with significant implications for congestion reduction, resource allocation, and sustainability. Traditional statistical models struggle to capture the complex spatiotemporal dependencies in traffic data, leading to reduced accuracy. Graph Neural Networks (GNNs) have emerged as a more practical approach due to their ability to model these intricate relationships. However, GNN-based methods face challenges in hyperparameter selection, which impacts their performance across diverse traffic scenarios. To address this, this paper proposes an adaptive traffic prediction model that uses reinforcement learning to optimize GNN hyperparameters. Our model significantly reduces the need for manual tuning and improves prediction accuracy across real-world traffic datasets, achieving a 9.8% reduction in Mean Absolute Error (MAE) and 3.6% improvement in Root Mean Squared Error (RMSE) compared to state-of-the-art baselines. In conclusion, dynamic hyperparameter adaptation boosts robustness and efficiency in traffic forecasting. This approach also helps us better understand how to fine-tune GNNs, contributing to the broader knowledge of optimizing machine learning models. Our work helps make traffic prediction more automated and improves how to manage urban transportation.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 431-440"},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592097","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":"Artificial intelligence-inspired comprehensive framework for desertification prediction","authors":"Shtwai Alsubai ,&nbsp;Mogeeb A.A. Mosleh ,&nbsp;Suheer A. Al-Hadhrami ,&nbsp;Munish Bhatia","doi":"10.1016/j.ijcce.2025.02.007","DOIUrl":"10.1016/j.ijcce.2025.02.007","url":null,"abstract":"<div><div>Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals.</div><div>Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification.</div><div>This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulating environmental sustainability policies.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 462-476"},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815342","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":"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":"2025-02-21","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":"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":"2025-02-21","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}

{"title":"Cognitive AI and implicit pseudo-spline wavelets for enhanced seismic prediction","authors":"Mutaz Mohammad","doi":"10.1016/j.ijcce.2025.02.003","DOIUrl":"10.1016/j.ijcce.2025.02.003","url":null,"abstract":"<div><div>Using data from 1900 to 2024, this study developed an innovative artificial intelligence (AI)-powered framework for predicting earthquakes in Japan. By incorporating state-of-the-art cognitive computing techniques with expert seismic assessments, the proposed algorithm addresses some of the complex challenges in earthquake prediction. The model fuses AI systems with numerical methods such as the Finite Element Method (FEM) and pseudo-spline collocation techniques to simulate seismic wave propagation in a stratified spherical Earth. This study employed cognitive computing mechanisms to categorize and analyze seismic activities using a vector-based structure that compares past seismic events with predefined classifications. Moreover, the framework integrates expert knowledge of the stress distribution in the Earth’s crust to establish a comprehensive model for seismic forecasting. This AI-driven methodology provides deeper insight into seismic wave behavior and introduces a self-improving data-centric system that could support decision-making for reducing earthquake risk.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 401-411"},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464921","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 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":"2025-02-17","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":"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":"2025-02-17","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}

{"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":"2025-02-07","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}