International Journal of Cognitive Computing in Engineering最新文献

Transforming legal texts into computational logic: Enhancing next generation public sector automation through explainable AI decision support 将法律文本转化为计算逻辑：通过可解释的人工智能决策支持增强下一代公共部门自动化

International Journal of Cognitive Computing in Engineering Pub Date : 2026-12-01 Epub Date: 2025-08-20 DOI: 10.1016/j.ijcce.2025.07.003

Markus Bertl , Simon Price , Dirk Draheim

{"title":"Transforming legal texts into computational logic: Enhancing next generation public sector automation through explainable AI decision support","authors":"Markus Bertl , Simon Price , Dirk Draheim","doi":"10.1016/j.ijcce.2025.07.003","DOIUrl":"10.1016/j.ijcce.2025.07.003","url":null,"abstract":"<div><div>This research presents a novel approach for translating legal texts into machine-executable computational logic to support the automation of public sector processes. Recognizing the high-stakes implications of artificial intelligence (AI) in legal domains, the proposed method emphasizes explainability by integrating explainable AI (XAI) techniques with natural language processing (NLP), employing scope-restricted pattern matching and grammatical parsing. The methodology involves several key steps: document structure inference from raw legal text, semantically neutral pre-processing, identification and resolution of internal and external references, contextualization of legal paragraphs, and rule extraction. The extracted rules are formalized as Prolog predicates and visualized as structured textual lists and graphical decision trees to enhance interpretability. To demonstrate the automatic extraction of explainable rules from legal text, we develop a Law-as-Code prototype and validate it through a real-world case study at the Austrian Ministry of Finance. The system successfully extracts executable rules from the Austrian <em>Study Funding Act</em>, confirming the feasibility and effectiveness of the proposed approach. This validation not only underscores the practical applicability of our method, but also highlights promising avenues for future research, particularly the integration of Generative AI and Large Language Models (LLMs) into the rule extraction pipeline, while preserving traceability and explainability.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 40-57"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932342","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}

引用次数: 0

Multilingual sentiment analysis in e-commerce customer reviews using GPT and deep learning-based weighted-ensemble model 基于GPT和基于深度学习的加权集成模型的电子商务客户评论多语言情感分析

International Journal of Cognitive Computing in Engineering Pub Date : 2026-12-01 Epub Date: 2025-11-15 DOI: 10.1016/j.ijcce.2025.10.003

Nuzhat Noor Islam Prova , Vishnu Ravi , Maninder Pal Singh , Vineet Kumar Srivastava , Srinivas Chippagiri , Arun Pratap Singh

{"title":"Multilingual sentiment analysis in e-commerce customer reviews using GPT and deep learning-based weighted-ensemble model","authors":"Nuzhat Noor Islam Prova , Vishnu Ravi , Maninder Pal Singh , Vineet Kumar Srivastava , Srinivas Chippagiri , Arun Pratap Singh","doi":"10.1016/j.ijcce.2025.10.003","DOIUrl":"10.1016/j.ijcce.2025.10.003","url":null,"abstract":"<div><div>As the e-commerce platforms grew exponentially, the volume of multilingual customer reviews increased, indicating that sentiment analysis is a priceless tool for finding consumer sentiment, enhancing marketing strategies, and improving customer experience. Nevertheless, emotion classification in multilingual reviews is very hard, and for one causes the variability of the language, the ambiguity of the sentiment, hierarchical word dependencies, and class imbalance, which can skew traditional models. In order to resolve such challenges, this paper introduces a T5-CapsNet ensemble model, which combines the T5 transformer for context-embedded feature extraction with Capsule Networks (CapsNet) for hierarchical sentiment learning. Furthermore, the model is further enhanced by a GAN-based data augmentation technique, which increases the number of minority class reviews in a dataset by adding synthetic minority class reviews in an effort to correct dataset imbalance and promote classification fairness. As an ensemble fusion strategy, weighted voting and stacking ensemble learning are used to improve sentiment prediction by making good use of the advantages of T5 and CapsNet. Experimental evaluations on the Multilingual Amazon Reviews Corpus (MARC) confirm that the proposed model surpasses the best sentiment classifier to reach an accuracy of 97.56%. It turns out that this hybrid deep learning approach very well captures the complex sentiment structures, or to put it differently, the multilingual e-commerce sentiment analysis largely benefited from such a hybrid deep learning approach. The findings from this study will be a foundation for building more advanced emotion classification models that can assist in improving customer sentiment analysis, automated feedback systems, as well as decision-making in global e-commerce ecosystems.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 268-286"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579041","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}

引用次数: 0

MABAC model based on linguistic (p, q)-rung orthopair fuzzy Z-number and their application in green supply chain management 基于语言（p, q）阶正交模糊z数的MABAC模型及其在绿色供应链管理中的应用

International Journal of Cognitive Computing in Engineering Pub Date : 2026-12-01 Epub Date: 2025-10-27 DOI: 10.1016/j.ijcce.2025.10.009

Zeeshan Ali , Dragan Pamucar , Vladimir Simic , Rajesh Kumar Dhanaraj

{"title":"MABAC model based on linguistic (p, q)-rung orthopair fuzzy Z-number and their application in green supply chain management","authors":"Zeeshan Ali , Dragan Pamucar , Vladimir Simic , Rajesh Kumar Dhanaraj","doi":"10.1016/j.ijcce.2025.10.009","DOIUrl":"10.1016/j.ijcce.2025.10.009","url":null,"abstract":"<div><div>The problem and complication arise from the growing environmental inefficiencies and concerns in traditional supply chains, for instance, poor accountability, excessive waste, and lack of transparency. The green supply chain practices aim to reduce or minimize the environmental impact of supply chain activities, but these efforts often face problems, for example, difficulty in monitoring sustainability performance, data manipulation, and limited traceability across numerous stakeholders. The main problem is that without effective techniques to verify and track eco-friendly practices, enterprises struggle to utilize and enforce green initiatives reliably. The blockchain technique is being derived as a solution because of its capability to give decentralized, transparent, and immutable records of processes and transactions. By integrating the blockchain into green supply chain practices, we aim to design the model of linguistic (p, q)-rung orthopair fuzzy Z-number sets with algebraic and Sugeno-Weber operational laws for the construction of the power weighted averaging operator and power weighted geometric operator. These operators can be used in the utilization of the multi-attributive border approximation area comparison model, which is also explained step-by-step with the help of examples to simplify the supremacy and validity of the invented model by comparing their ranking values with the ranking values of the existing approaches.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 247-267"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145527744","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}

引用次数: 0

Energy efficient mobile edge computing framework based on secure host level management for resource allocation 基于安全主机级资源分配管理的节能移动边缘计算框架

International Journal of Cognitive Computing in Engineering Pub Date : 2026-12-01 Epub Date: 2025-11-21 DOI: 10.1016/j.ijcce.2025.11.004

Ananda Babu Jayachandra , S.K. Suhas , Ahmed Alkhayyat , Abdul Lateef Haroon Phulara Shaik , S.P. Paramesh

{"title":"Energy efficient mobile edge computing framework based on secure host level management for resource allocation","authors":"Ananda Babu Jayachandra , S.K. Suhas , Ahmed Alkhayyat , Abdul Lateef Haroon Phulara Shaik , S.P. Paramesh","doi":"10.1016/j.ijcce.2025.11.004","DOIUrl":"10.1016/j.ijcce.2025.11.004","url":null,"abstract":"<div><div>In Mobile Edge Computing (MEC), task scheduling is used to overcome the problems related to resource constraints in mobile devices. However, the conventional task allocation approaches failed to consider both the energy consumption and security requirements, that leads to higher energy consumption, increased latency and potential vulnerabilities. To overcome these issues, this research proposes a secure and energy-aware task scheduling technique by considering energy and security needs at host level while allocating tasks to Virtual Machines (VMs). The evaluation of energy cost at task allocation helps to enhance the energy efficiency. On the contrary, security is improved via the combination of authentication, confidentiality, and integrity mechanism that ensures secure task execution while maintaining low energy consumption. Thus, the proposed model i.e., multi-tier MEC architecture performs secure resource allocation and reduced energy utilization through effective VM selection and optimized scheduling. The results are evaluated based on energy consumption, average delay, and make span time. For instance, when the result is evaluated by considering energy consumption, the proposed approach consumed 7.3 J of energy when time slot is assigned as 10, while the existing Improved NSGA-II consumed higher energy of 8.2 J in the similar time slot.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 300-309"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623220","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}

引用次数: 0

Blockchain-based decentralized smart healthcare using improved wild horse optimizer with Graph Convolutional Autoencoder in IoT environment 基于区块链的分散式智能医疗，在物联网环境中使用改进的野马优化器和图卷积自编码器

International Journal of Cognitive Computing in Engineering Pub Date : 2026-12-01 Epub Date: 2025-10-24 DOI: 10.1016/j.ijcce.2025.10.007

José Escorcia-Gutierrez , Melitsa Torres-Torres , Roosvel Soto-Diaz , Carlos Soto

{"title":"Blockchain-based decentralized smart healthcare using improved wild horse optimizer with Graph Convolutional Autoencoder in IoT environment","authors":"José Escorcia-Gutierrez , Melitsa Torres-Torres , Roosvel Soto-Diaz , Carlos Soto","doi":"10.1016/j.ijcce.2025.10.007","DOIUrl":"10.1016/j.ijcce.2025.10.007","url":null,"abstract":"<div><div>The Internet of Things (IoT) continues to expand by incorporating physical devices, software, computing systems, and hardware that facilitate communication and data exchange. Its integration into healthcare, specifically in the realm of smart healthcare, has contributed significantly to the rise of big data within the medical field. The adoption of IoT-enabled wearable technologies by healthcare professionals aims to streamline diagnosis and treatment processes. However, security and privacy concerns associated with data storage and transmission pose significant challenges to the efficacy and trustworthiness of these systems. To address these concerns, this article presents a blockchain-assisted centralized smart healthcare framework, which utilizes the Improved Wild Horse Optimizer (IWHO) and Graph Convolutional Autoencoder (GCAE) in an IoT environment to ensure secure and accurate disease detection. The BIWHO-GCAE framework consists of three main components: Inception v3-based feature extraction, IWHO-based hyperparameter tuning, and GCAE-based classification. The experimental evaluation, conducted using the benchmark skin lesion dataset, shows that the BIWHO-GCAE method outperforms current state-of-the-art deep learning models, demonstrating improvements of 2.62% in accuracy, 3.07% in sensitivity, and 7.28% in specificity. These results highlight the potential of the BIWHO-GCAE framework to enhance diagnostic performance while ensuring the security and privacy of healthcare data in decentralized IoT-based systems.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 199-212"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465978","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}

引用次数: 0

A novel channel estimation of MIMO-OFDM using hybrid bionic binary spotted hyena optimization 基于混合仿生二元斑点鬣狗优化的MIMO-OFDM信道估计

International Journal of Cognitive Computing in Engineering Pub Date : 2026-12-01 Epub Date: 2025-09-08 DOI: 10.1016/j.ijcce.2025.09.003

C. Premila Rosy , S. Yazhinian , M. Therasa , K.R. Surendra , Anand Karuppannan , A. Manikandan

{"title":"A novel channel estimation of MIMO-OFDM using hybrid bionic binary spotted hyena optimization","authors":"C. Premila Rosy , S. Yazhinian , M. Therasa , K.R. Surendra , Anand Karuppannan , A. Manikandan","doi":"10.1016/j.ijcce.2025.09.003","DOIUrl":"10.1016/j.ijcce.2025.09.003","url":null,"abstract":"<div><div>A promising generalized inverse discrete Fourier transform non-orthogonal frequency division multiplexing (GIDFT-OFDM) system can satisfy the requirement of supporting higher data rates in fifth-generation (5G) technology. However, this system has a high peak-to-average power ratio (PAPR) because many subcarrier signals are transmitted. The inverse discrete Fourier transform (IDFT) is used in an orthogonal frequency-division multiplexing (OFDM) modulator to convert symbols from the frequency domain to the time domain and add a cyclic prefix before sending them through the channel. In pilot-based channel estimation, pilots are inserted into the transmitter and detected at the receiver, along with the OFDM symbols. In this study, we searched for local and global optimal solutions of the Bionic Binary Spotted Hyena Optimization (BBSHO) algorithm with position coordinate vectors (PCVs) of social behavior. It also introduces the BBSHO algorithm to improve the local search capability within the search space. Optimized pilots provided better performance than orthogonal and randomly placed pilots. The stochastic, quadrature, and whale swarm algorithms detect the position of the pilot. To improve the data quality and reduce the BER, MSE, and SER, we introduced several optimization algorithms on the channels of MIMO-OFDM devices. The performance of the two optimization algorithms proposed above contrasts with that of the current simple algorithms and shows improved results in MIMO-OFDM networks. The proposed optimization algorithm was implemented using the MATLAB 2021(a) software. For channel optimization, metaheuristic algorithms such as the Whale Swarm Algorithm (WSA) and the Hybrid Bionic Binary Spotted Hyena Optimization (BBSHO) algorithm are used.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 95-103"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157631","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}

引用次数: 0

Bibliometric analysis of author count, funding, and citations in AI research 人工智能研究中作者数量、资助和引用的文献计量分析

International Journal of Cognitive Computing in Engineering Pub Date : 2026-12-01 Epub Date: 2025-09-26 DOI: 10.1016/j.ijcce.2025.09.004

Wei-Chao Lin , Huei-Hua Tsao , You-Shyang Chen , Chien-Lung Hsu

{"title":"Bibliometric analysis of author count, funding, and citations in AI research","authors":"Wei-Chao Lin , Huei-Hua Tsao , You-Shyang Chen , Chien-Lung Hsu","doi":"10.1016/j.ijcce.2025.09.004","DOIUrl":"10.1016/j.ijcce.2025.09.004","url":null,"abstract":"<div><div>The academic community places significant emphasis on publishing research in SCI and SSCI journals, which are known for their credibility, high quality, and strong reputations. Most research requires significant investment in human resources and equipment, making the acquisition of research funding crucial. Understanding the motivation behind authorship and its association with citation patterns in SCI and SSCI journals represents a significant research concern in bibliometric studies. This study identifies the relationship among author number, research funding, and citation count using content analysis techniques, including the chi-square and analysis of variance tests. Investment in AI research, development, and applications is increasing; thus, this study examines 4488 articles published in the field of artificial intelligence (AI) from Springer in 2018. The empirical results indicate that (1) the average number of authors is highest in Q1 journals, with non-single-author papers being more common than single-author papers and concentrated in higher rankings; (2) papers with research funding are more common than those without; (3) papers with citations are more frequent than those without; (4) the ranking of papers with research funding and citations is higher than that of other papers without funding; and (5) the average citation count of papers with research funding leads in Q1 and is higher than in other rankings. This study is the first attempt at highlighting papers in the field of AI from Springer. The results and important findings provide useful references for researchers, reviewers, publishers, and interested parties with different purposes for academic and technical publications with sustained success. This study uniquely integrates four dimensions—author count, research funding, journal ranking, and citation count—to offer novel insights into academic publishing performance in the AI field.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 104-117"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219125","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}

引用次数: 0

OcclusionNetPlusPlus: a multi-scale similarity network with adaptive occlusion detection for robust iris recognition OcclusionNetPlusPlus：基于自适应遮挡检测的多尺度相似性网络，用于鲁棒虹膜识别

International Journal of Cognitive Computing in Engineering Pub Date : 2026-12-01 Epub Date: 2025-09-05 DOI: 10.1016/j.ijcce.2025.09.002

Rahul Tanna , Tanish Patel , Faisal Mohammed Alotaibi , Rutvij H. Jhaveri , Thippa Reddy Gadekallu

{"title":"OcclusionNetPlusPlus: a multi-scale similarity network with adaptive occlusion detection for robust iris recognition","authors":"Rahul Tanna , Tanish Patel , Faisal Mohammed Alotaibi , Rutvij H. Jhaveri , Thippa Reddy Gadekallu","doi":"10.1016/j.ijcce.2025.09.002","DOIUrl":"10.1016/j.ijcce.2025.09.002","url":null,"abstract":"<div><div>A significant challenge in iris recognition systems is the presence of occlusions affecting the iris, face, and periocular regions. To address this issue, this study proposes an OcclusionNetPlusPlus framework which employs carefully designed bank of Gabor filters to capture iris texture patterns at different scales and orientations. We then inject 2D positional encodings into these filter responses to embed explicit (x,y) location information, enabling downstream modules to reason about where each feature came from. The innovation in our approach is the introduction of an occlusion detection mechanism that generates probability maps based on local variance analysis, effectively identifying occluded regions in the iris image. These probability maps are used to dynamically weight the extracted features, reducing the influence of unreliable regions during similarity computation. The framework incorporates a custom loss function that optimizes feature similarity while maintaining discriminative power across different iris patterns. Training and evaluation were conducted on publicly available iris recognition datasets, ensuring a diverse test bed for assessing performance across different occlusion scenarios. We evaluated OcclusionNetPlusPlus on CASIA-Iris-Thousand and IIT Delhi V1.0. In controlled tests, it achieves an EER of 0.51 %, an FRR of 0.54 % at FAR = 1 % (0.61 % at FAR = 0.1 %), and a d-prime of 7.04. Even under simulated unconstrained conditions—adding noise, blur, and random occlusions—EER stays around 2 %.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 74-85"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048422","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}

引用次数: 0

Intelligent evaluation algorithm for Taekwondo poomsae quality and posture recognition teaching system 跆拳道弹射质量与姿势识别教学系统的智能评价算法

International Journal of Cognitive Computing in Engineering Pub Date : 2026-12-01 Epub Date: 2025-11-04 DOI: 10.1016/j.ijcce.2025.11.001

Dandan Li , Shihao Guo

{"title":"Intelligent evaluation algorithm for Taekwondo poomsae quality and posture recognition teaching system","authors":"Dandan Li , Shihao Guo","doi":"10.1016/j.ijcce.2025.11.001","DOIUrl":"10.1016/j.ijcce.2025.11.001","url":null,"abstract":"<div><div>Taekwondo is a traditional martial art that originated in South Korea, which not only promotes a healthy lifestyle, but also enhances self-discipline and confidence. The quality of Taekwondo education, especially poomsae (forms) practice, largely depends on subjective judgments by coaches or referees, resulting in inconsistent evaluations and lengthy processes. Therefore, the study proposes a dynamic human key point detection model based on Graph Convolutional Networks (GCN). This model simplifies human actions by constructing a spatiotemporal map of human key points, extracting behavioral features through the GCN, and identifying Taekwondo poomsae actions. Experimental results show that when the dataset size was 800, the accuracy of the designed model reached 0.98, which was better than that of other models such as the predictive encoding GCN (0.90), behavior structure GCN (0.88), and skeleton behavior recognition GCN (0.83). Furthermore, the designed model achieved a low Root Mean Square Error (RMSE) of 0.10, while other models had RMSE of 0.15, 0.19, and 0.28, respectively. The operation time of the designed model was 4.6 seconds, demonstrating its superior efficiency and accuracy in detecting and recognizing Taekwondo poomsae. This study demonstrates the feasibility of integrating artificial intelligence with Taekwondo teaching to enhance the quality of training.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 235-246"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528224","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}

引用次数: 0

Enhancing web of things security using Harris hawks optimization with reinforcement learning 使用哈里斯鹰优化和强化学习增强物联网安全

International Journal of Cognitive Computing in Engineering Pub Date : 2026-12-01 Epub Date: 2026-01-07 DOI: 10.1016/j.ijcce.2026.01.001

Mohammad Alauthman , Ahmad Al-Qerem , Ammar Almomani , Abdelraouf M. Ishtaiwi , Amjad Aldweesh , Mohammad Arafah , Varsha Arya , Brij B. Gupta

{"title":"Enhancing web of things security using Harris hawks optimization with reinforcement learning","authors":"Mohammad Alauthman , Ahmad Al-Qerem , Ammar Almomani , Abdelraouf M. Ishtaiwi , Amjad Aldweesh , Mohammad Arafah , Varsha Arya , Brij B. Gupta","doi":"10.1016/j.ijcce.2026.01.001","DOIUrl":"10.1016/j.ijcce.2026.01.001","url":null,"abstract":"<div><div>The Web of Things (WoT) interconnects a rapidly growing population of smart devices and sensors, enabling innovative applications while exposing an ever‑expanding attack surface. Reinforcement learning (RL) can adaptively detect and mitigate such attacks, yet conventional RL struggles to converge in WoT’s high‑dimensional state‑action spaces. We address this limitation by augmenting RL with the Harris Hawks Optimization (HHO) algorithm. HHO is a recent meta‑heuristic optimization method that balances global exploration with local exploitation and is well suited to large search spaces. We propose an HHO‑based meta‑learning framework that aims to identify hyper‑parameters and network architecture for a deep‑Q network (DQN) defender, maximizing average episodic reward in simulated WoT environments. Experiments on the CIC‑IoT‑2023 and Bot‑IoT datasets show that an HHO‑optimized DQN converges faster and achieves higher accuracy than all tested baselines—including vanilla, double and dueling DQNs, PPO, A3C and Transformer-based agents—illustrating the promise of bio-inspired optimization for adaptive WoT security.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 376-388"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037154","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}

引用次数: 0