IF 7.7 1区工程技术

IEEE Transactions on Industrial Electronics Pub Date : 2025-06-04 DOI: 10.1109/tie.2025.3569948

Xin Wang, Zhixue Zheng, Michel Aillerie, Marie-Cecile Pera, Daniel Hissel

引用次数: 0

Privacy-Preserving Image Retrieval in Cloud Computing via Adaptive Secret Keys and Self-Supervised Block-Augmented Pretraining 基于自适应密钥和自监督块增强预训练的云计算中隐私保护图像检索

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-06-04 DOI: 10.1109/tsc.2025.3576692

Qihua Feng, Zhixun Lu, Litian Zhang, Chaozhuo Li, Feiran Huang, Jian Weng, Philip S. Yu

引用次数: 0

A Hybrid NOMA-OMA Framework for Multi-User Offloading in Mobile Edge Computing System 移动边缘计算系统中多用户卸载的混合NOMA-OMA框架

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-06-04 DOI: 10.1109/tsc.2025.3576699

Furong Chai, Qi Zhang, Haipeng Yao, Xiangjun Xin, Ran Gao, Di Wu, F. Richard Yu

引用次数: 0

Spatial Re-parameterization for N:M Sparsity N:M稀疏性的空间再参数化

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-06-04 DOI: 10.1109/tpami.2025.3572240

Yuxin Zhang, Mingbao Lin, Mingliang Xu, Yonghong Tian, Rongrong Ji

引用次数: 0

Belief-based Fuzzy and Imprecise Clustering for Arbitrary Data Distributions 任意数据分布的基于信念的模糊不精确聚类

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-06-04 DOI: 10.1109/tfuzz.2025.3576588

Zuo-wei Zhang, Zhun-ga Liu, Liang-bo Ning, Hong-peng Tian, Bing-lu Wang

引用次数: 0

A Knowledge Distillation-based Approach to Speech Emotion Recognition 基于知识提取的语音情感识别方法

IF 11.2 2区计算机科学

IEEE Transactions on Affective Computing Pub Date : 2025-06-04 DOI: 10.1109/taffc.2025.3574178

Ziping Zhao, Jixin Liu, Haishuai Wang, Danushka Bandara, Jianhua Tao

引用次数: 0

Ensemble machine learning models for estimating mechanical curves of concrete-timber-filled steel tubes 预估钢管混凝土力学曲线的集成机器学习模型

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-06-04 DOI: 10.1016/j.engappai.2025.111234

F. Kazemi , N. Asgarkhani , T. Ghanbari-Ghazijahani , R. Jankowski

{"title":"Ensemble machine learning models for estimating mechanical curves of concrete-timber-filled steel tubes","authors":"F. Kazemi , N. Asgarkhani , T. Ghanbari-Ghazijahani , R. Jankowski","doi":"10.1016/j.engappai.2025.111234","DOIUrl":"10.1016/j.engappai.2025.111234","url":null,"abstract":"<div><div>The use of concrete-timber-filled steel tubes (CTFSTs) as composite structural elements in buildings is gaining attraction among researchers due to their positive structural behavior and high load-bearing capacity. The combination of steel, concrete and timber materials improves energy absorption and ductility making CTFSTs a promising choice for modern construction. However, finding the mechanical properties of CTFSTs is a challenge during the design process due to the complexity of predicting behavior. Reliable modeling of these interactions is essential for an optimal design, which requires extensive experimental data and advanced computational methodologies. Therefore, this study proposed ensemble machine learning (ML) models for estimating load-displacement and stress-strain curves as well as the maximum axial capacity and elastic stiffness of CTFSTs. The results confirm the reliability of ensemble ML models for predicting the elastic stiffness and the maximum axial capacity of CTFST specimens with error percentages of 0.57 and 0.72, respectively. In addition, proposed ensemble ML models were used to estimate axial load-displacement and stress-strain curves of CTFSTs having different shapes of timber, which their curve fitting ability were superior compared to other ML models (i.e., accuracy of 97.6 %). Having ensemble ML models validated by experimental tests, a graphical user interface (GUI) tool is prepared for the preliminary evaluation of CTFST specimens, which can ease the way for reducing the experimental costs.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"156 ","pages":"Article 111234"},"PeriodicalIF":7.5,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204995","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

Fire hawks optimized radial basis function neural network based feature extraction and ON/OFF detection of household appliances 火鹰优化了基于径向基函数神经网络的家电特征提取与开/关检测

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-04 DOI: 10.1016/j.compeleceng.2025.110441

Deepika Rohit Chavan, Dagadu Shankar More

{"title":"Fire hawks optimized radial basis function neural network based feature extraction and ON/OFF detection of household appliances","authors":"Deepika Rohit Chavan, Dagadu Shankar More","doi":"10.1016/j.compeleceng.2025.110441","DOIUrl":"10.1016/j.compeleceng.2025.110441","url":null,"abstract":"<div><div>Accurate ON/OFF detection of household appliances is essential for smart energy monitoring, lowering costs, and improving energy efficiency in smart homes. However, existing ON/OFF detection methods have several challenges, such as high computational complexity, overfitting and overlapping power usage patterns, which lead to false classifications and reduced performance. This study proposes a novel hybrid method combining a Fire Hawks optimized radial basis function neural network (FH_RBFNN) in order to extract and detect ON/OFF status at the source end of a residential building. The Fire Hawks Optimization Algorithm (FHO) is employed to fine-tune Radial Basis Function Neural Network (RBFNN) layer parameters, which ensures effective feature extraction by reducing redundancy. Subsequently, the Xtreme Gradient Boosting (XGBoost) technique is employed to classify the extracted features in order to identify the ON/OFF stage of house appliances. The proposed FH_RBFNN+ XGBoost model achieves high detection performance in terms of accuracy of 0.995, Precision of 0.99324, Recall of 0.99606, F1-Score of 0.99465, and Specificity of 0.99067, respectively.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110441"},"PeriodicalIF":4.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

IEEE Computational Intelligence Society Information IEEE计算智能学会信息

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2025-06-04 DOI: 10.1109/TCDS.2025.3553653

引用次数: 0

Interaction With Virtual Objects Using Human Pose and Shape Estimation 使用人体姿态和形状估计与虚拟物体的交互

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2025-06-04 DOI: 10.1002/cav.70046

Hong Son Nguyen, DaEun Cheong, Andrew Chalmers, Myoung Gon Kim, Taehyun Rhee, JungHyun Han

引用次数: 0

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