Computational Intelligence最新文献_第10页

Energy optimization with authentication and cost effective storage in the wireless sensor IoTs using blockchain 利用区块链在无线传感器物联网中通过认证和低成本存储实现能源优化

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-02-13 DOI: 10.1111/coin.12630

Turki Ali Alghamdi, Nadeem Javaid

{"title":"Energy optimization with authentication and cost effective storage in the wireless sensor IoTs using blockchain","authors":"Turki Ali Alghamdi, Nadeem Javaid","doi":"10.1111/coin.12630","DOIUrl":"https://doi.org/10.1111/coin.12630","url":null,"abstract":"In this paper, a hybrid blockchain-based authentication scheme is proposed that provides the mechanism to authenticate the randomly distributed sensor IoTs. These nodes are divided into three types: ordinary nodes, cluster heads and sink nodes. For authentication of these nodes in a Wireless Sensor IoTs (WSIoTs), a hybrid blockchain model is introduced. It consists of both private and public blockchains, which are used to authenticate ordinary nodes and cluster heads, respectively. Moreover, to handle the issue of cluster head failure due to inefficient energy consumption, Improved Heterogeneous Gateway-based Energy-Aware Multi-hop Routing (I-HMGEAR) protocol is proposed in combination with blockchain. It provides a mechanism to efficiently use the overall energy of the network. Besides, the processed data of subnetworks is stored on blockchain that causes the issue of increased monetary cost. To solve this issue, an external platform known as InterPlanetary File System (IPFS) is used, which distributively stores the data on different devices. The simulation results show that our proposed model outperforms existing clustering scheme in terms of network lifetime and data storage cost of the WSIoTs. Our proposed scheme increases the lifetime of the network as compared to existing trust management model, intrusion prevention and multi WSN authentication schemes by 17.5%, 24.2% and 19.6%, respectively.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139732347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence and Internet of Things-enabled decision support system for the prediction of bacterial stalk root disease in maize crop 人工智能和物联网决策支持系统用于预测玉米作物细菌性茎根病

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-02-13 DOI: 10.1111/coin.12632

Shaha Al-Otaibi, Rahim Khan, Jehad Ali, Aftab Ahmed

{"title":"Artificial intelligence and Internet of Things-enabled decision support system for the prediction of bacterial stalk root disease in maize crop","authors":"Shaha Al-Otaibi, Rahim Khan, Jehad Ali, Aftab Ahmed","doi":"10.1111/coin.12632","DOIUrl":"https://doi.org/10.1111/coin.12632","url":null,"abstract":"Although the Internet of Things (IoT) has been considered one of the most promising technologies to automate various daily life activities, that is, monitoring and prediction, it has become extremely useful for problem solving with the introduction and integration of artificial intelligence (AI)-enabled smart learning methodologies. Therefore, due to their overwhelming characteristics, AI-enabled IoTs have been used in different application environments, such as agriculture, where detection, prevention (if possible), and prediction of crop diseases, especially at the earliest possible stage, are desperately required. Bacterial stalk root is a common disease of tomatoes that severely affects its production and yield if necessary measures are not taken. In this article, AI and an IoT-enabled decision support system (DSS) have been developed to predict the possible occurrence of bacterial stalk root diseases through a sophisticated technological infrastructure. For this purpose, Arduino agricultural boards, preferably with necessary embedded sensors, are deployed in the agricultural field of maize crops to capture valuable data at a certain time interval and send it to a centralized module where AI-based DSS, which is trained on an equally similar data set, is implemented to thoroughly examine captured data values for the possible occurrence of the disease. Additionally, the proposed AI- and IoT-enabled DSS has been tested on benchmark data sets, that is, freely available online, along with real-time captured data sets. Both experimental and simulation results show that the proposed scheme has achieved the highest accuracy level in timely prediction of the underlined disease. Finally, maize crop plots with the proposed system have significantly increased the yield (production) ratio of crops.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139732247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction to Capacitated single-allocation hub location model for a flood relief distribution network 对泄洪分配网络的有能力单一分配枢纽位置模型的更正

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-01-23 DOI: 10.1111/coin.12614

引用次数: 0

XAI-driven model for crop recommender system for use in precision agriculture 用于精准农业的 XAI 驱动型作物推荐系统模型

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-01-14 DOI: 10.1111/coin.12629

Parvathaneni Naga Srinivasu, Muhammad Fazal Ijaz, Marcin Woźniak

{"title":"XAI-driven model for crop recommender system for use in precision agriculture","authors":"Parvathaneni Naga Srinivasu, Muhammad Fazal Ijaz, Marcin Woźniak","doi":"10.1111/coin.12629","DOIUrl":"10.1111/coin.12629","url":null,"abstract":"Agriculture serves as the predominant driver of a country's economy, constituting the largest share of the nation's manpower. Most farmers are facing a problem in choosing the most appropriate crop that can yield better based on the environmental conditions and make profits for them. As a consequence of this, there will be a notable decline in their overall productivity. Precision agriculture has effectively resolved the issues encountered by farmers. Today's farmers may benefit from what's known as precision agriculture. This method takes into account local climate, soil type, and past crop yields to determine which varieties will provide the best results. The explainable artificial intelligence (XAI) technique is used with radial basis functions neural network and spider monkey optimization to classify suitable crops based on the underlying soil and environmental conditions. The XAI technology would provide assets in better transparency of the prediction model on deciding the most suitable crops for their farms, taking into account a variety of geographical and operational criteria. The proposed model is assessed using standard metrics like precision, recall, accuracy, and F1-score. In contrast to other cutting-edge approaches discussed in this study, the model has shown fair performance with approximately 12% better accuracy than the other models considered in the current study. Similarly, precision has improvised by 10%, recall by 11%, and F1-score by 10%.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139483901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Event assigning based on hierarchical features and enhanced association for Chinese mayor's hotline 基于分层特征和增强关联的中国市长热线事件分配

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-01-04 DOI: 10.1111/coin.12626

Gang Chen, Xiaomin Cheng, Jianpeng Chen, Xiangrong She, JiaQi Qin, Jian Chen

引用次数: 0

HyperED: A hierarchy-aware network based on hyperbolic geometry for event detection HyperED：基于双曲几何的事件检测分层感知网络

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-01-04 DOI: 10.1111/coin.12627

Meng Zhang, Zhiwen Xie, Jin Liu, Xiao Liu, Xiao Yu, Bo Huang

{"title":"HyperED: A hierarchy-aware network based on hyperbolic geometry for event detection","authors":"Meng Zhang, Zhiwen Xie, Jin Liu, Xiao Liu, Xiao Yu, Bo Huang","doi":"10.1111/coin.12627","DOIUrl":"10.1111/coin.12627","url":null,"abstract":"Event detection plays an essential role in the task of event extraction. It aims at identifying event trigger words in a sentence and classifying event types. Generally, multiple event types are usually well-organized with a hierarchical structure in real-world scenarios, and hierarchical correlations between event types can be used to enhance event detection performance. However, such kind of hierarchical information has received insufficient attention which can lead to misclassification between multiple event types. In addition, the most existing methods perform event detection in Euclidean space, which cannot adequately represent hierarchical relationships. To address these issues, we propose a novel event detection network HyperED which embeds the event context and types in Poincaré ball of hyperbolic geometry to help learn hierarchical features between events. Specifically, for the event detection context, we first leverage the pre-trained BERT or BiLSTM in Euclidean space to learn the semantic features of ED sentences. Meanwhile, to make full use of the dependency knowledge, a GNN-based model is applied when encoding event types to learn the correlations between events. Then we use a simple neural-based transformation to project the embeddings into the Poincaré ball to capture hierarchical features, and a distance score in hyperbolic space is computed for prediction. The experiments on MAVEN and ACE 2005 datasets indicate the effectiveness of the HyperED model and prove the natural advantages of hyperbolic spaces in expressing hierarchies in an intuitive way.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139094283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A mechanism for network resource allocation and task offloading in mobile edge computing and network engineering 移动边缘计算和网络工程中的网络资源分配和任务卸载机制

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-01-03 DOI: 10.1111/coin.12628

Zhixu Shu, Kewang Zhang

引用次数: 0

Joint optimization of UAV position and user grouping for UAV-assisted hybrid NOMA systems 为无人机辅助混合 NOMA 系统联合优化无人机位置和用户分组

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-01-02 DOI: 10.1111/coin.12625

Yuan Sun, Zhicheng Dong, Liuqing Yang, Donghong Cai, Weixi Zhou, Yanxia Zhou

{"title":"Joint optimization of UAV position and user grouping for UAV-assisted hybrid NOMA systems","authors":"Yuan Sun, Zhicheng Dong, Liuqing Yang, Donghong Cai, Weixi Zhou, Yanxia Zhou","doi":"10.1111/coin.12625","DOIUrl":"10.1111/coin.12625","url":null,"abstract":"This article investigates the use of unmanned aerial vehicles (UAVs) in assisting hybrid non-orthogonal multiple access (NOMA) systems to enhance spectrum efficiency and communication connectivity. A joint optimization problem is formulated for UAV positioning and user grouping to maximize the sum rate. The formulated problem exhibits non-convexity, calling for an effective solution. To address this issue, a two-stage approach is proposed. In the first stage, a particle swarm optimization algorithm is employed to optimize the UAV positions without considering user grouping. With the UAV positions optimized, a game theory-based approach is utilized in the second stage to optimize user grouping and improve the sum rate of the hybrid NOMA system. Simulation results demonstrate that the proposed two-stage method achieves solutions close to the global optimum of the original problem. By optimizing the positions of UAVs and user groups, the sum rate can be effectively improved. Additionally, optimizing the deployment of UAVs ensures better fairness in providing communication services to multiple users.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139077260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated indoor positioning methods to optimize computations and prediction accuracy enhancement 优化计算和提高预测精度的综合室内定位方法

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-01-02 DOI: 10.1111/coin.12620

Yongho Kim, Jiha Kim, Cheolwoo You, Hyunhee Park

{"title":"Integrated indoor positioning methods to optimize computations and prediction accuracy enhancement","authors":"Yongho Kim, Jiha Kim, Cheolwoo You, Hyunhee Park","doi":"10.1111/coin.12620","DOIUrl":"10.1111/coin.12620","url":null,"abstract":"Indoor GPS location estimation encounters accuracy challenges from intricate building structures and diverse signal interferences. Trilateration methods utilising APs are typically employed to estimate indoor locations. Nevertheless, estimation errors from multipath effects and high power consumption of sensors employed in location estimation curtail battery life. To address this issue, research into location estimation methods utilising machine learning has been conducted. However, challenges involving the selection of the optimal access point locations and obtaining dense RSSI data have been noted. In this article presents a solution based on sparse radio maps for decreasing the expenses of collecting RSSI data while simultaneously enhancing indoor location accuracy through the integration of image data. The proposed approach integrates matrix-based RSSI indoor positioning (M-RIP) for initial location estimation and feature-based image indoor positioning (F-IIP) for position determination via image feature matching. Furthermore, extended area-based post-processing (EA-PP) is employed to augment M-RIP's precision and minimize image matching computation in F-IIP, improving overall performance. This article utilizes actual building data to validate the precision of the position estimation and efficiency of computation reduction using the proposed method.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139094493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Guest Editorial on the Special Issue on the Role of Fuzzy Systems on Biomedical Science in Healthcare 模糊系统在医疗保健领域生物医学科学中的作用特刊客座编辑

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2024-01-01 DOI: 10.1111/coin.12623

Davide Moroni, Maria Trocan, Behçet Uğur Töreyin

引用次数: 0