Engineering Applications of Artificial Intelligence最新文献_第9页

HK-MOEA/D: A historical knowledge-guided resource allocation for decomposition multiobjective optimization HK-MOEA/D：分解多目标优化的历史知识指导下的资源分配

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-05 DOI: 10.1016/j.engappai.2024.109482

Wei Li , Xiaolong Zeng , Ying Huang , Yiu-ming Cheung

{"title":"HK-MOEA/D: A historical knowledge-guided resource allocation for decomposition multiobjective optimization","authors":"Wei Li , Xiaolong Zeng , Ying Huang , Yiu-ming Cheung","doi":"10.1016/j.engappai.2024.109482","DOIUrl":"10.1016/j.engappai.2024.109482","url":null,"abstract":"<div><div>Decomposition-based multiobjective evolutionary algorithms is one of the prevailing algorithmic frameworks for multiobjective optimization. This framework distributes the same amount of evolutionary computing resources to each subproblems, but it ignores the variable contributions of different subproblems to population during the evolution. Resource allocation strategies (RAs) have been proposed to dynamically allocate appropriate evolutionary computational resources to different subproblems, with the aim of addressing this limitation. However, the majority of RA strategies result in inefficiencies and mistakes when performing subproblem assessment, thus generating unsuitable algorithmic results. To address this problem, this paper proposes a decomposition-based multiobjective evolutionary algorithm (HK-MOEA/D). The HK-MOEA/D algorithm uses a historical knowledge-guided RA strategy to evaluate the subproblem’s evolvability, allocate evolutionary computational resources based on the evaluation value, and adaptively select genetic operators based on the evaluation value to either help the subproblem converge or move away from a local optimum. Additionally, the density-first individual selection mechanism of the external archive is utilized to improve the diversity of the algorithm. An external archive update mechanism based on <span><math><mi>θ</mi></math></span>-dominance is also used to store solutions that are truly worth keeping to guide the evaluation of subproblem evolvability. The efficacy of the proposed algorithm is evaluated by comparing it with seven state-of-the-art algorithms on three types of benchmark functions and three types of real-world application problems. The experimental results show that HK-MOEA/D accurately evaluates the evolvability of the subproblems and displays reliable performance in a variety of complex Pareto front optimization problems.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109482"},"PeriodicalIF":7.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Compact representation for memory-efficient storage of images using genetic algorithm-guided key pixel selection 利用遗传算法引导的关键像素选择实现图像存储记忆效率的紧凑表示法

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109540

Samir Malakar, Nirwan Banerjee, Dilip K. Prasad

{"title":"Compact representation for memory-efficient storage of images using genetic algorithm-guided key pixel selection","authors":"Samir Malakar, Nirwan Banerjee, Dilip K. Prasad","doi":"10.1016/j.engappai.2024.109540","DOIUrl":"10.1016/j.engappai.2024.109540","url":null,"abstract":"<div><div>In the past few years, we have observed rapid growth in digital content. Even in the biological domain, the arrival of microscopic and nanoscopic images and videos captured for biological investigations increases the need for space to store them. Hence, storing these data in a storage-efficient manner is a pressing need. In this work, we have introduced a compact image representation technique with an eye on preserving the shape that can shrink the memory requirement to store. The compact image representation is different from image compression since it does not include any encoding mechanism. Rather, the idea is that this mechanism stores the positions of key pixels, and when required, the original image can be regenerated. The genetic algorithm is used to select key pixels, while the Gaussian kernel performs the reconstruction task with the help of the positions of the selected key pixels. The model is tested on four different datasets. The proposed technique shrinks the memory requirement by 87% to 98% while evaluated using the bit reduction rate. However, the reconstructed images’ quality is a bit low when evaluated using metrics like structural similarity index (ranges between 0.81 to 0.94), or root means squared error (ranges between 0.06 to 0.08). To investigate the impact of quality reduction in reconstructed images in real-life applications, we performed image classification using reconstructed samples and found 0.13% to 2.30% classification accuracy reduction compared to when classification is done using original samples. The proposed model’s performance is comparable to state-of-the-art’s similar solutions.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109540"},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Color-aware fusion of nighttime infrared and visible images 夜间红外图像与可见光图像的色彩感知融合

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109521

Jiaxin Yao , Yongqiang Zhao , Yuanyang Bu , Seong G. Kong , Xun Zhang

{"title":"Color-aware fusion of nighttime infrared and visible images","authors":"Jiaxin Yao , Yongqiang Zhao , Yuanyang Bu , Seong G. Kong , Xun Zhang","doi":"10.1016/j.engappai.2024.109521","DOIUrl":"10.1016/j.engappai.2024.109521","url":null,"abstract":"<div><div>Pixel-level fusion of visible and infrared images has demonstrated promise in enhancing information representation. However, nighttime image fusion remains challenging due to low and uneven lighting. Existing fusion methods neglect the preservation of color-related information at night, resulting in unsatisfactory outcomes with insufficient brightness. This paper presents a novel color image fusion framework to prevent color distortion, thus generating results more aligned with human perception. Firstly, we design an image fusion network to retain color information from visible images under low-light conditions. Secondly, we incorporate mature low-light enhancement technology into the network as a flexible component to produce fusion results under normal illumination. The training process is carefully designed to address potential issues of overexposure or noise amplification. Finally, we utilize knowledge distillation to create a lightweight end-to-end network that directly generates fusion results under normal lighting conditions from pairs of low-light images. Experimental results demonstrate that our proposed framework outperforms existing methods in nighttime scenarios.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109521"},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Combining model-based and learning-based anomaly detection schemes for increased performance and safety of aircraft braking controllers 结合基于模型和基于学习的异常检测方案，提高飞机制动控制器的性能和安全性

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109551

José Joaquín Mendoza Lopetegui, Mara Tanelli

{"title":"Combining model-based and learning-based anomaly detection schemes for increased performance and safety of aircraft braking controllers","authors":"José Joaquín Mendoza Lopetegui, Mara Tanelli","doi":"10.1016/j.engappai.2024.109551","DOIUrl":"10.1016/j.engappai.2024.109551","url":null,"abstract":"<div><div>In aircraft, the braking system is a safety-critical and heavily used component of the landing gear, prone to significant wear. Anomalies arising in the wear dynamics can degrade the performance of the braking system and compromise the safety of ground handling maneuvers. In this work, we tackle the problem of detecting incipient anomalies in aircraft brakes in a tightly coupled implementation with the Brake Control Unit (BCU). Two complementary approaches are presented. The first one is an observer-based architecture designed on the longitudinal aircraft dynamics that returns physically interpretable outputs connected to the wear process and allows us to improve braking performance online. The second one is an end-to-end convolutional autoencoder-based architecture that returns an anomaly score computed on data collected by the BCU with inherent robustness to modeling uncertainty, which the model-based one does not. A combined architecture that allows one to exploit the features of both model-based and learning-based approaches is proposed, which shows its capability of optimally blending the two. The approaches are evaluated in a MATLAB/Simulink multibody simulation environment that is able to replicate the braking actuator wear dynamics, demonstrating remarkable performances in anomaly detection, anti-skid control performance, and safety improvement.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109551"},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Picture fuzzy complex proportional assessment approach with step-wise weight assessment ratio analysis and criteria importance through intercriteria correlation 图片模糊复合比例评估法与分步权重评估比率分析以及通过标准间相关性确定标准重要性

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109554

Ayesha Razzaq , Zareen A. Khan , Khalid Naeem , Muhammad Riaz

{"title":"Picture fuzzy complex proportional assessment approach with step-wise weight assessment ratio analysis and criteria importance through intercriteria correlation","authors":"Ayesha Razzaq , Zareen A. Khan , Khalid Naeem , Muhammad Riaz","doi":"10.1016/j.engappai.2024.109554","DOIUrl":"10.1016/j.engappai.2024.109554","url":null,"abstract":"<div><div>The concept of the picture fuzzy set (PiFS) significantly enhances the multi-criteria decision-making (MCDM) process by incorporating membership value (MV), non-membership value (NMV), and a neutral component. PiFS extends the capabilities of traditional fuzzy sets (FSs), intuitionistic fuzzy sets (IFSs), and other fuzzy models. This paper introduces a novel MCDM approach, the picture fuzzy SWARA-CRITIC-COPRAS (PiF-SCC) method, specifically designed to assist decision-makers (DMs) in evaluating and selecting dynamic digital marketing (DDM) technologies within PiFS settings. The proposed method integrates the strengths of PiFS with step-wise weight assessment ratio analysis (SWARA), criteria importance through intercriteria correlation (CRITIC), and complex proportional assessment (COPRAS), aiming to improve the precision and effectiveness of technology evaluations. To validate the approach, a case study is conducted on DDM technology assessment within a specific business context. The PiF-SCC technique is applied to rank technological options using linguistic terms (LTs), PiFS numbers, an accuracy function (AF), and a score function (SF). Additionally, a comprehensive sensitivity analysis is performed to evaluate the robustness of the proposed method under different input scenarios and uncertainties. A thorough comparison with existing techniques is also provided, demonstrating the superior decision-making capability of the new approach, which leads to more accurate and dependable technology selection results. The manuscript also discusses marginal implications and limitations, along with potential future research directions to further enhance the applicability and effectiveness of the proposed approach.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109554"},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Investigation of hybrid modeling and its transferability in building load prediction used for district heating systems 混合建模及其在区域供热系统建筑负荷预测中的可移植性研究

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109544

Ning Zhang , Wei Zhong , Xiaojie Lin , Liuliu Du-Ikonen , Tianyue Qiu

{"title":"Investigation of hybrid modeling and its transferability in building load prediction used for district heating systems","authors":"Ning Zhang , Wei Zhong , Xiaojie Lin , Liuliu Du-Ikonen , Tianyue Qiu","doi":"10.1016/j.engappai.2024.109544","DOIUrl":"10.1016/j.engappai.2024.109544","url":null,"abstract":"<div><div>In the district heating systems, the historical operation data of the buildings in those areas would be partially or entirely missing. The traditional data-driven model is hard to predict the ground truth results because the historical data is not available for model training. However, utilizing the physics-based methods for load calculation takes a long time to process and encounters low accuracy issues. This paper investigates several hybrid models that integrate the data-driven model and the physics-based models with different fusion methods. The physics-based models calculate envelope load and infiltration load, based on Fourier's law and the grand canonical ensemble theory, respectively. After undergoing load processing, features fusion, and residual connection, the best advanced hybrid models generate 21.35%, 16.35%, and 12.73% better prediction results compared with the data-driven model. Moreover, the advanced hybride models also perform strong transferability across all the data quantity groups. In terms of practical application, the advanced hybrid models could be deployed with effective generalization in limited data scenarios and robust transfer capabilities. The selected best model constructed by hybrid modeling displays the highest performance and saves the total training costs with strong transferability.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109544"},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 deep learning ensemble approach for malware detection in Internet of Things utilizing Explainable Artificial Intelligence 利用可解释人工智能的深度学习集合方法检测物联网中的恶意软件

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109560

Saksham Mittal , Mohammad Wazid , Devesh Pratap Singh , Ashok Kumar Das , M. Shamim Hossain

{"title":"A deep learning ensemble approach for malware detection in Internet of Things utilizing Explainable Artificial Intelligence","authors":"Saksham Mittal , Mohammad Wazid , Devesh Pratap Singh , Ashok Kumar Das , M. Shamim Hossain","doi":"10.1016/j.engappai.2024.109560","DOIUrl":"10.1016/j.engappai.2024.109560","url":null,"abstract":"<div><div>The Internet of Things (IoT) has been popularized these days due to digitization and automation. It is deployed in various applications, i.e., smart homes, smart agriculture, smart transportation, smart healthcare, and industrial monitoring. In an IoT network, many IoT devices communicate with servers, or users access IoT devices through an open channel via a certain exchange of messages. Besides providing many benefits like efficiency, automation, and convenience, IoT presents significant security challenges due to a lack of proper standard security measures. Thus, malicious actors may be able to infect the network with malware. They may launch destructive attacks with the goal of stealing data or causing damage to the systems’ resources. This can be mitigated by introducing intrusion detection and prevention mechanisms in the network. An intelligent intrusion detection system is required to put preventative measures in place for secure communication and a malware-free network. In this article, we propose a deep learning based ensemble approach for IoT malware attack detection (in short, we call it as DLEX-IMD) trained and tested against benchmark datasets. The important measures, including accuracy, precision, recall, and F1-score, are used to evaluate the performance of the proposed DLEX-IMD. The performance of the proposed scheme is explained utilizing benchmark Explainable Artificial Intelligence (AI) method–LIME (Local Interpretable Model-Agnostic Explanations), which justifies the reliability of the proposed model training. The DLEX-IMD is also compared with a range of other closely related existing schemes and has shown better performance than those schemes with 99.96% accuracy and F1-score of 0.999.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109560"},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

TopoPIS: Topology-constrained pipe instance segmentation via adaptive curvature convolution TopoPIS：通过自适应曲率卷积进行拓扑约束管道实例分割

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109547

Jia Hu, Jianhua Liu, Shaoli Liu

{"title":"TopoPIS: Topology-constrained pipe instance segmentation via adaptive curvature convolution","authors":"Jia Hu, Jianhua Liu, Shaoli Liu","doi":"10.1016/j.engappai.2024.109547","DOIUrl":"10.1016/j.engappai.2024.109547","url":null,"abstract":"<div><div>Precise and fast pipe instance segmentation is a critical component in industrial automatic assembly, facilitating accurate object detection and pose estimation, optimizing and supervising the assembly process. However, this problem is challenging due to topological errors on fine-scale structures caused by the pipes being complex and slender. To address these challenges, we propose a topology-constrained pipe instance segmentation network (TopoPIS) for complex stacking scene to achieve accurate segmentation with topological correctness. To better extract the features of complex and variable morphological pipes, adaptive curvature convolution is introduced to dynamically adapt to the slender pipe structure and capture critical features. To handle topological errors like broken connections, we propose a novel topological constraint loss function based on persistent homology, which greatly improves the topological continuity of the segmentation. Experimental results on real-world and unseen datasets demonstrate that our TopoPIS outperforms other methods regrading segmentation accuracy and topological continuity.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109547"},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Optimizing and predicting swarming collective motion performance for coverage problems solving: A simulation-optimization approach 优化和预测蜂群集体运动性能以解决覆盖问题：模拟优化方法

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109522

Reda Ghanem , Ismail M. Ali , Shadi Abpeikar , Kathryn Kasmarik , Matthew Garratt

{"title":"Optimizing and predicting swarming collective motion performance for coverage problems solving: A simulation-optimization approach","authors":"Reda Ghanem , Ismail M. Ali , Shadi Abpeikar , Kathryn Kasmarik , Matthew Garratt","doi":"10.1016/j.engappai.2024.109522","DOIUrl":"10.1016/j.engappai.2024.109522","url":null,"abstract":"<div><div>Algorithms using swarming collective motion can solve coverage problems in unknown environments by reacting to unknown obstacles in real-time when they are encountered. However, these algorithms face two key challenges when deployed on real robots. First, hand-tuning efficient collective motion parameters is both time-consuming and difficult. Second, predicting the time required for a swarm to solve a particular problem is not straightforward. This paper introduces a novel evolutionary framework to address both problems by proposing a methodology that autonomously tunes collective motion parameters for coverage problems while predicting the time required for real robots to complete the task. Our approach utilizes a simulation–optimization framework that employs a genetic algorithm to optimize the parameters of a frontier-led swarming algorithm. Results indicate that the optimized parameters are transferable to real robots, achieving 100% coverage while maintaining 84% connectivity between them. Compared to state-of-the-art swarm methods, our system reduced turnaround time by 50% and 57% in different environments while maintaining collective motion. It also achieved a 55% reduction in turnaround time on average across five scenarios compared to budget-constrained path planning, with a 10% increase in coverage. Furthermore, our framework outperformed both hand-tuned and learned collective motion approaches, reducing turnaround time by 73% in non-collective motion scenarios and by 63% while maintaining 85% connectivity in collective motion scenarios. This approach effectively combines the adaptability of swarm behavior with the predictive reliability of planning methods.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109522"},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diophantine spherical vague sets and their applications for micro-technology robots based on multiple-attribute decision-making 基于多属性决策的 Diophantine 球形模糊集及其在微型技术机器人中的应用

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109447

Murugan Palanikumar , Nasreen Kausar , Željko Stević , Sarfaraz Hashemkhani Zolfani

{"title":"Diophantine spherical vague sets and their applications for micro-technology robots based on multiple-attribute decision-making","authors":"Murugan Palanikumar , Nasreen Kausar , Željko Stević , Sarfaraz Hashemkhani Zolfani","doi":"10.1016/j.engappai.2024.109447","DOIUrl":"10.1016/j.engappai.2024.109447","url":null,"abstract":"<div><div>We introduce the concept of Diophantine spherical vague set approach to multiple-attribute decision-making. The Spherical vague set is a novel expansion of the vague set and interval valued spherical fuzzy set. Three new concepts have been introduce such as Diophantine spherical vague weighted averaging operator, Diophantine spherical vague weighted geometric operator, generalized Diophantine spherical vague weighted averaging operator and generalized Diophantine spherical vague weighted geometric operator. We provide a numerical example to show how Euclidean distance and Hamming distance interact. Applications of the Diophantine spherical vague number include idempotency, boundedness, commutativity and monotonicity in algebraic operations. They can determine the optimal option and are more well-known and reasonable. Our goal was to identify the optimal choice by comparing expert opinions with the criteria. As a result, the model’s output was more accurate as well as in the range of the natural number <figure><img></figure>. The weighted averaging distance and weighted geometric distance operators are distance measure that is based on aggregating model. By comparing the models under discussion with those suggested in the literature, we hoped to show their worth and reliability. It is possible to find a better solution more quickly, simply, and practically. Our objective was to compare the expert evaluations with the criteria and determine which option was the most suitable. Because they yield more precise solutions, these models are more accurate and more related to models with <figure><img></figure>. To show the superiority and the validity of the proposed aggregation operations, we compared it with the existing method and concluded from the comparison and sensitivity analysis that our proposed technique is more effective and reliable. This investigation yielded some intriguing results.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109447"},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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