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International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Alkhafaji, Mohammed Ayad Yamsani, Nagendar Rashmi, P. Babu, N.V. National University of Science and Technology Dhi Qar Iraq School of Computer Science and Artificial Intelligence SR University Warangal India Inventuriz Labs Pvt. Ltd. Bengaluru India Department of Electronics and Communication Engineering SJB Institute of Technology Bengaluru India

ISBN: (纸本)9789819778751

Wireless Sensor Networks (WSNs) consists of number of wireless sensor nodes located around the geographical locations. In the deployment of WSN, there is required to know the senor deployment details in prior. The large number of data captured through the Sensor Node (SN) utilizes the SN’s minimum energy as well as confuse the data analysis at Base Station or sink for the decision making. The essential accurate evaluation of the parameters such as distance among the nodes, network lifetime, utilization of number of nodes as well as communication channel parameters. In this research, the Machine Learning (ML) algorithms of ensemble Artificial Neural Network (ANN) and Support Vector Machine (SVM) is proposed for speed up the parameters evaluations with efficient accurate results. The performance of the proposed ANN-SVM method attains better results and it achieves the network lifetime of 8345, prediction error of 0.09, Mean Square Error (MSE) of 0.005637, Mean Absolute Error (MAE) of 0.064571 and Root Mean Square Error (RMSE) of 0.07356 when compared to the existing methods such as Support Vector Regression (SVR) and Deep Neural Network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mean square error

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A Hybrid Machine Learning Approach for Top-N Bike Recommendations

A Hybrid Machine Learning Approach for Top-N Bike Recommenda...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Kumar, V. Vinoth Raj, T. Selvin Retna Raj, J. Relin Francis Sornavalli, M. Krishnan, R. Santhana Soundiraraj, N. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India Dmi College of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Mathematics Madurai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India

ISBN: (纸本)9798331509828

This research introduces a bike recommendation system aimed at providing personalized suggestions by integrating diverse data sources. The system leverages information from social media, user reviews, and product specifications to generate a comprehensive dataset. Social media interactions, such as likes, shares, and comments, offer valuable insights into the popularity and user engagement of various bike models. E-commerce platforms contribute detailed information regarding bike features, including price, brand, type, and user ratings, enhancing the dataset's depth. The data undergoes preprocessing to ensure consistency and relevance, which allows for effective feature extraction and the handling of missing values. This step is crucial in ensuring the quality of the dataset, which is then used to train the recommendation models. The system combines multiple recommendation techniques to tailor suggestions based on individual user preferences and behavior. By integrating content-based filtering and collaborative filtering, the model generates highly relevant recommendations suited to each user's specific needs. The models are evaluated using key performance metrics, such as accuracy, precision, recall, and F1 score, to ensure the most effective algorithms are selected for the task. This evaluation process guarantees that the system delivers high-quality recommendations. Once trained, the recommendation models are deployed via an efficient API, enabling real-time, personalized bike suggestions to users. The system's web interface allows users to input their preferences, such as budget, bike type, and other specifications, further enhancing the personalization of recommendations. Overall, this research demonstrates the value of integrating multiple data sources to create an advanced, personalized bike recommendation system capable of meeting diverse user needs. © 2025 IEEE.

关键词： Bike recommendation system data integration e-commerce data personalized suggestions social media data user reviews

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Distributed Modulation Recognition for IoT Devices in Data-Limited Applications

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IEEE Internet of Things Journal 2025年

作者： Xu, Fenghua Zhu, Yukun Yang, Feng Zhang, Xiaosong Mu, Junsheng Chen, Hsiao-Hwa University of Electronic Science and Technology of China School of Computer Science and Engineering China Beijing University of Posts and Telecommunications School of Information and Communication Engineering China National Cheng Kung University Department of Engineering Science Taiwan

Deep learning (DL) has been widely utilized in automatic modulation classification (AMC), and its performance depends largely on the presence of high-quality datasets. Motivated by this fact, this work addresses the AMC challenges in data-limited IoT environments, proposing a framework combining few-shot meta-learning and federated learning for resource-constrained devices, where edge nodes use meta-learning for training with global updates via federated averaging (FedAvg). The system aggregates samples from multiple nodes while still maintaining data security. Simulations involved 11 modulation types with varying SNRs, 100 client nodes, and 10 rounds of federated learning. The iterative process includes loading pre-trained parameters, performing local training, averaging local parameters, and updating global parameters. The obtained results show 70% post-training testing accuracy, with a consistently good performance during federated iterations. The results demonstrated the effectiveness of the proposed framework in data-scarce IoT scenarios, offering a robust performance across varying signal qualities while minimizing energy consumption and communication overhead, which is crucial for IoT device longevity and network scalability, highlighting framework’s potential for real-world applications in distributed modulation recognition. © 2014 IEEE.

关键词： Signal modulation

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High-temperature bond strength evaluation of concrete overlays with industrial by-products: Experimental and analytical approaches using machine learning

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engineering Applications of Artificial Intelligence 2025年 153卷

作者： Javid, Alireza Javid, Erfan Toufigh, Vahab Department of Civil Engineering The Sharif University of Technology Tehran Iran School of Civil Engineering Iran University of Science and Technology Tehran Iran

This paper presents a comprehensive study on the bond strength of concrete overlays incorporating industrial by-products such as fly ash and ground granulated blast furnace slag under high-temperature conditions. The research involved 540 concrete specimens exposed to temperatures of 25 °C, 100 °C, 300 °C, 500 °C, and 700 °C and cured for 14, 28, 60, and 90 days. Results demonstrated the beneficial roles of fly ash incorporation in overlays in improving the thermal stability and bond strength between concrete substrates and repair material overlays at high temperatures. Utilizing artificial intelligence techniques, a set of advanced machine learning models was created to accurately predict bond strength properties, specifically compressive, tensile, and shear strengths. The models evaluated included gradient boosting, extreme gradient boosting, light gradient boosting, and categorical boosting. Additionally, a multilayer perceptron (MLP) neural network was used as a baseline for comparison. The categorical boosting model demonstrated superior performance, achieving a root mean square error of 2.570, mean absolute error of 1.792, and R2 of 0.989 for predicting compressive strength in the test dataset. It also achieved similarly high accuracy for tensile and shear strength, with R2 values of 0.927 and 0.942, respectively. The SHapley Additive exPlanations analysis highlighted the significant impact of the predictive models on temperature, curing time, density, and porosity. Monte Carlo simulations further ensured robust model predictions and insights. The findings, accessible through a user-friendly web application (https://***/), provide valuable insights into optimizing material compositions and enhancing the durability of repaired concrete structures in extreme environments. This study demonstrates the efficiency and reliability of artificial intelligence in bond strength evaluation of heat-da

关键词： Compression testing

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Proximity electronic effect of adjacent Ni Site enhances compatibility of hydrogenation and deoxygenation over Cu Site to boost nitrate electroreduction to ammonia

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Chinese Journal of Catalysis 2025年第3期70卷 285-298页

作者： Xue-Feng Cheng Qing Liu Qi-Meng Sun Huilong Dong Dong-Yun Chen Ying Zheng Qing-Feng Xu Jian-Mei Lu College of Chemistry Chemical Engineering and Materials ScienceCollaborative Innovation Center of Suzhou Nano Science and TechnologySoochow UniversitySuzhou 215123JiangsuChina School of Chemistry and Chemical Engineering Huaiyin Normal UniversityHuai'an 223300JiangsuChina Department of Chemical and Biochemical Engineering Western UniversityLondon N6A37KOntarioCanada School of Materials Engineering Changshu Institute of TechnologyChangshu 215500JiangsuChina

Electrocatalytic conversion of nitrate to ammonia(NITRR)can simultaneously achieve the removal of nitrate and the synthesis of value-added ammonia,a promising candidate to replace Haber-Bosch process with low carbon dioxide ***,high hydrogenation energy barrier for*NO intermediates and insufficient supply of active hydrogen cause slow hydrogenation process,and further result in low efficiency of nitrate conversion and ammonia ***,a series of tandem catalysts,one-dimensional coordination polymers(1D CCPs)with dual sites are synthesized and obtained 190.4 mg h^(-1)mgcat^(-1)ammonia production rate with Faradaic efficiency of 97.16%,outperforming to the most of recent reported *** catalytic performances are well-maintained even after a long-term stability test of 1200 h,laying the foundation for practical *** functional theory results reveal that the stationary adsorbed*NO on Ni site induced proximity electronic effect could reduce the energy barrier for hydrogenation of*NO intermediates over Cu *** addition,the Ni site in the dual sites 1D CCPs is conducive to generating active hydrogen,providing rich proton source to boost the hydrogenation of*NO,and further enhancing the compatibility of deoxygenation and hydrogenation *** work paves a new insight into the mechanism of NITRR process and will inspire more research interests in exploring the proximity electronic effect in catalytic process.

关键词： Electrocatalysis Ammonia synthesis Nitrate reduction Proximity electronic effect Dual sites

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A novel high-protein composite rice with anti-glycation properties prepared with crushed rice flour, whey protein and lotus seed proanthocyanidins

Food Biomacromolecules

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Food Biomacromolecules 2025年第1期2卷 23-34页

作者： Chenxu Bao Zhiyong Niu Boqian He Yinxin Li Siyu Han Nianjie Feng Hunag Huang Chao Wang Jingyi Wang Qian Wu School of Life and Health Sciences Hubei University of Technology Wuhan China School of Material Science & Chemical Engineering Hubei University of Technology Wuhan China

In this research, a novel rice composed of grated rice flour, whey protein, and lotus seedpod oligomeric procyanidins was developed through pre-gelatinization combined with twin-screw extrusion techniques. The nutritional, physicochemical properties and biological activities of the composite rice were systematically studied. Results revealed that the composite rice exhibited a 17.6% reduction in cooking time, a 23.6% increase in gelatinization degree, and a 51.8% decrease in rehydration water absorption compared to conventional rice. Meanwhile, compared to commercial rice, the composite rice showed superior elasticity, cohesiveness, chewability and resilience, alongside reduced hardness and viscosity. Notably, it displayed improved resistance to enzymatic digestion. Nutritionally, the contents of protein, fat, total phenol and proanthocyanidins were higher than those of commercial rice. Furthermore, it showed better antioxidant activity and advanced glycosylation end-products (AGEs) inhibitory activity. This research addresses the growing demand for nutritionally balanced rice products and advances innovative strategies for the application of composite raw materials in functional food development.

关键词： lotus seedpod proanthocyanidins quality and bioactivity rice flour starch-protein-polyphenol composite food whey protein

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Numerical simulation of the effect of Faraday cage on the inductively coupled plasma discharge

Numerical simulation of the effect of Faraday cage on the in...

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2024 International Conference on Aerospace, Mechanical and Materials engineering, AMME 2024

作者： Lei, Fan Zhang, Zhaoyang Wei, Xiaolong Han, Xinmin Wu, Hongzhi Liu, Zhiwei School of Electrical and Control Engineering Shaanxi University of Science and Technology Xi'an710016 China National Defense Key Laboratory of Aeronautical Plasma Dynamics Air Force Engineering University Xi'an710038 China China Academy of Aerospace Science and Innovation Beijing100032 China

Taking inductively coupled plasma (ICP) as the research object, a method of increasing the electron density of ICP by Faraday cage electromagnetic shielding is proposed. At the same time, the introduction of the Faraday cage can effectively prevent the electromagnetic interference caused by the discharge coil, which is helpful in reproducing the real near-space plasma environment in the laboratory. Based on COMSOL Multiphysics, a two-dimensional inductively coupled plasma discharge model was constructed, and the effect of the Faraday cage on ICP discharge parameters was comparatively studied. The simulation results show that the Faraday cage can effectively shield the electromagnetic interference caused by the discharge coil. At the same time, the Faraday cage can increase the electron density and the electron temperature of the plasma to a certain extent, and the plasma is evenly distributed in this case. With the increase of discharge power, the electromagnetic shielding effect of the Faraday cage is still effective, and the effect on the plasma electron density is more obvious. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Inductively coupled plasma

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Why do CuAl catalysts outperform in CO_(2) electro-reduction to C_(2)H_(4)?

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science China Chemistry 2025年第2期68卷 763-771页

作者： Dongfang Cheng Gong Zhang Lulu Li Xiangcheng Shi Wenjin Zhu Xintong Yuan Lyudmila Moskaleva Peng Zhang Zhi-Jian Zhao Jinlong Gong Key Laboratory for Green Chemical Technology of Ministry of Education School of Chemical Engineering and TechnologyTianjin University Collaborative Innovation Center of Chemical Science and Engineering(Tianjin)Tianjin 300072China Joint School of National University of Singapore and Tianjin University International Campus of Tianjin UniversityFuzhou 350207China Department of Chemistry University of the Free StateBloemfontein 9301South Africa Haihe Laboratory of Sustainable Chemical Transformations Tianjin 300192China National Industry-Education Platform of Energy Storage Tianjin UniversityTianjin 300350China International Joint Laboratory of Low-carbon Chemical Engineering Tianjin 300192China Tianjin Normal University Tianjin 300387China

Highly efficient CO_(2) electroreduction(CO_(2)RR)holds the promise of closing the carbon ***,the strategies for enhancing activity and selectivity for single C2+ products are still *** paper describes the oxophilicity of doped metals on copper(Cu)as a key descriptor for predicting the selectivity in the production of ethylene and ethanol in CO_(2)*** findings reveal a volcano-like relationship between the*O binding energy and catalytic activity,with aluminum(Al)-doped Cu alloys situated at the *** strong Al–O bond facilitates C–O bond cleavage,thereby promoting C_(2)H_(4) *** grand canonical density functional theory(GCDFT)calculations,we elucidate the significance of strong oxygen affinity in enhancing*CO_(2) adsorption,leading to its activation into a bidentate-adsorbed ***,our results demonstrate that Al doping promotes C–C coupling by altering the adsorption mode of the key intermediate *OCCO,shifting it from a less favorable boattype to a more stable chair-type *** modification significantly contributes to the improved catalytic activity for C2+products on CuAl *** research provides a detailed mechanistic insight into the high activity and selectivity observed in CO_(2)RR to ethylene on CuAl catalysts,underscoring the critical role of metal oxophilicity in enhancing CO_(2)RR performance.

关键词： CO_(2)RR dilute alloy descriptor surface oxophilicity

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Fuzzy-Based Optimization Techniques for Segmenting the Tumors in Multimodal MRI Images

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Operations Research Forum 2025年第1期6卷 1-27页

作者： Alagarsamy, Saravanan Nagarajan, D. Govindaraj, Vishnuvardhan Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Rajiv Gandhi Salai (OMR) Tamilnadu Chennai India Department of Mathematics Rajalakshmi Institute of Technology Chennai India Division of Data Science of School of Computing Science and Education VIT Bhopal University Kothrikalan Madhya Pradesh Sehore 466114 India

Accurate brain tumor prediction is essential when approaching the field of health care;wherever accuracy in decision-making is crucial, the issues also need to be resolved right away. Many artificial intelligence and machine learning-based techniques have been developed in the last couple of years in the field of healthcare. The intention of this work is to create an algorithm that combines the features of Crow Search (CS) and Interval Type-II Fuzzy Logic System (IT2FLS) algorithms to distinguish the area of tumor from complex brain tissues. The ability of oncologists to make decisions is critical for any therapy sequence to be successful, and the methodology proposed in the research work considerably influences a conclusion through technology interruption. The suggested approach is flexible enough to work with a diversity of image sequences found in the BRATS Challenge 2020 dataset that presents varying degrees of obstacles, challenges, and difficulties in locating the anomalous regions, and it produces improved demarcation results that have been instinctively evaluated and supported by Dice score (98 ± 1.3), specificity (98 ± 1.4), and sensitivity (98 ± 1.7) as average metrics. The motive of this study is the enhancement of the visual perception of oncologists, which gives them improved insight into and comprehension of the condition of the patient. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Crow Search (CS) Interval Type-II Fuzzy Logic System (IT2FLS) Magnetic Resonance Imaging (MRI) Tumor segmentation

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Formation mechanism of cerium-based inclusions and effect on microstructure and impact toughness in Q355ME

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Materialwissenschaft und Werkstofftechnik 2025年第3期56卷 353-363页

作者： Zhang, X. Zhi, J. Song, X. An, S. School of Materials Science and Engineering Inner Mongolia University of Science and Technology Baotou014010 China Technical Center of Inner Mongolia BaoTou Steel Union Co. Ltd. Baotou014010 China Inner Mongolia Enterprise Key Laboratory of Rare Earth Steel Products Research & Development Baotou014010 China

The study explored the formation mechanisms of cerium-based inclusions and effects on the microstructure and impact toughness of industrial Q355ME steel through cerium addition. Formation of the inclusions was governed by both thermodynamic and kinetic factors. The results revealed that inclusions in cerium-free steel primarily consisted of magnesium aluminum spinel (with lesser manganese sulpfide), calcium aluminates (CaAl4O7, CaAl12O19), and calcium sulpfide with lesser aluminum oxide. Conversely, in cerium-containing steel, the inclusions predominantly included manganese sulpfide (with lesser magnesium aluminum spinel and a combination of cerium (III) sulpfide and cerium oxysulpfide), cerium (III) sulpfide-cerium oxysulpfide-calcium sulpfide (with lesser magnesium aluminum spinel) and calcium sulpfide. The introduction of cerium led to the disappearance of calcium aluminum oxide inclusions, producing smaller, more numerous, and spherical or ellipsoidal complex cerium-based inclusions. Furthermore, cerium addition resulted in a reduction in both the quantity and size of manganese sulpfide inclusions. Cerium-bearing complex inclusions contributed to grain refinement. Both transverse and longitudinal toughness values for cerium-containing steel were similar at identical temperatures, with longitudinal toughness surpassing transverse toughness in samples without cerium. Under identical conditions, especially at −80 °C, the impact toughness values of steel with cerium were significantly higher than those without cerium. © 2025 Wiley-VCH GmbH.

关键词： Grain refinement

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