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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Eugenio, F. Mederos-Barrera, A. Marcello, J. ULPGC Inst Oceanog & Cambio Global IOCAG Parque Cient Tecnol Marino Taliarte S-N Las Palmas Gran Canaria 35214 Spain

ISBN: (纸本)9798350320107

Coastal island ecosystems are unique and fragile environments and very sensitive to climate change and direct anthropogenic impact. The use of remote sensing offers the advantage of monitoring these valuable areas in an accessible and cost-effective manner. The main objective of this research, linked to the sustainable management of littoral areas, is the generation of knowledge that is materialized in the implementation of a robust image processing methodology to generate accurate bathymetry and benthic high-resolution maps in coastal shallow waters using remote sensing satellite multispectral sensors (WorldView-2/3). So, this paper presents a methodology for the monitoring of two protected ecosystems in Canary Islands (Spain): Las Canteras beach (Gran Canaria Island) and the channel of La Graciosa-Lanzarote Island. In addition, a multitemporal study is presented where the usefulness of the technology in the monitoring of marine ecosystems is presented.

关键词： Bathymetric mapping benthic mapping coastal ecosystems high-resolution satellite artificial intelligence algorithms

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Analysis of Rural Tourism Route Planning System Based on artificial intelligence Algorithm 3

Analysis of Rural Tourism Route Planning System Based on Art...

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3rd International Conference on artificial intelligence and Autonomous Robot Systems, AIARS 2024

作者： Feng, Xiumei Qi, Haixia Guo, Lei Northeast Petroleum University Department of Management Qinhuangdao Hebei066000 China

ISBN: (纸本)9798350376173

Under the background of the era of tourism informatization and artificial intelligence (AI), higher requirements are adopted for the route planning of scenic spots. In this paper, in the rural tourism route planning system based on artificial intelligence algorithm, the rural tourism route planning system based on artificial intelligence algorithm is registered, and the information of scenic spots and users is saved. The tourism planning route selection module is used to select one from at least one tourism planning route, generate the text of travel plan and plan it intelligently. In the comparison of per capita tourism consumption of tourists of different ages before and after adopting this system, before adopting this system, the per capita tourism consumption of 18-30 years old was 2500 yuan, and after adopting this system, the per capita tourism consumption of 18-30 years old was 2000 yuan. This method can give full play to the advantages of rural areas, promote the development of rural tourism, and at the same time effectively promote the local economy, thus achieving the goal of rural revitalization. © 2024 IEEE.

关键词： artificial intelligence algorithms percapita tourism consumption route planning systems Rural tourism tourist attributes

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Design of Children's Digital Education Resource Sharing Platform Based on artificial intelligence Algorithm

Design of Children's Digital Education Resource Sharing Plat...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Wang, Yibo School of Literature and Media Chongqing Second Normal University Chongqing China

ISBN: (纸本)9798350309614

With the rapid development of digital technology, the application of digital educational resources in children's education is becoming increasingly widespread. This paper aims to design a children's digital educational resource sharing platform based on artificial intelligence algorithms to provide high-quality educational resources and personalized learning methods through deep learning algorithms. Firstly, the paper introduces the basic knowledge of deep learning and common convolutional neural network architectures. Then, it describes in detail the technical design of the platform, including the application of HTML5, PHP language, and the relational database MySQL. Finally, the paper designs and implements the children's digital educational resource sharing platform based on artificial intelligence algorithms, and describes the system architecture and major functional implementation of the platform. In addition, the paper discusses the possibility of further development of the platform in the future, and emphasizes the importance of the children's digital educational resource sharing platform in improving educational quality and meeting the needs of personalized learning. © 2023 IEEE.

关键词： artificial intelligence algorithms children's education deep learning algorithms resource sharing platforms

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Quantum-inspired metaheuristic algorithms for Industry 4.0: A scientometric analysis

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ENGINEERING APPLICATIONS OF artificial intelligence 2025年 139卷

作者： Pooja Sood, Sandeep Kumar Natl Inst Technol Kurukshetra Dept Comp Applicat Kurukshetra 136119 Haryana India

Quantum-inspired Metaheuristic algorithms have redefined non-deterministic polynomial time hard optimization challenges by leveraging quantum mechanics principles. These algorithms herald a broad range of application scenarios in Industry 4.0 and offer feasible time solutions for complex, large-scale industrial landscapes. The potential benefits provided by the quantum-inspired metaheuristic algorithms have accelerated the scientific advancements in this domain. Consequently, the present research contributes to the existing knowledge base by presenting the intellectual landscape through scientometric and systematic literature analysis. The study is conducted on the dataset derived from the Scopus and Web of Science databases, covering 2001 to 2023. The study employs co-citation and co-occurrence analyses to discern prominent research topics, emerging research frontiers, significant authors, and the most collaborating countries. The research findings underscore that electric vehicles, energy efficiency, and combinatorial optimization are prominent research topics, while carbon emission, resource management, and path planning are burgeoning areas of exploration in this knowledge domain. The intricate and entangled network linkage determines that the research community in this domain fosters a dynamic and synergistic relationship. Overall, the pivotal insights and the research challenges articulated in this article offer valuable insights to researchers and the academic community, aiding in discerning the intellectual terrain and emerging research patterns in quantum-inspired metaheuristic algorithms. This, in turn, fosters the advancement of innovation and facilitates well-informed decision-making within this evolving research paradigm.

关键词： Quantum-inspired Large-scale industrial optimization Non-deterministic polynomial time hard optimization Quantum computing artificial intelligence algorithms

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Using evolutionary algorithms and group method of data handling ANN for prediction of the viscosity MWCNT-ZnO /oil SAE 50 nano-lubricant

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2025年 163卷

作者： Liu, Zuozhi Ali, Ali B. M. Hussein, Rasha Abed Singh, Narinderjit Singh Sawaran Al-Bahrani, Mohammed Abdullaeva, Barno Saeidlou, Salman Salahshour, Soheil Esmaeili, Sh. Guizhou Univ Finance & Econ Sch Math & Stat Guiyang 550025 Guizhou Peoples R China Univ Warith Al Anbiyaa Coll Engn Air Conditioning Engn Dept Karbala Iraq Al Manara Coll Med Sci Dept Dent Amarah Maysan Iraq INTI Int Univ Fac Data Sci & Informat Technol Nilai 71800 Malaysia Al Mustaqbal Univ Dept Chem Engn & Petr Ind Babylon 51001 Iraq Tashkent State Pedag Univ Dept Math & Informat Technol Sci Affairs Tashkent Uzbekistan Canterbury Christ Church Univ Sch Engn Technol & Design Mech Mat Engn Canterbury CT1 1QU Kent England Istanbul Okan Univ Fac Engn & Nat Sci Istanbul Turkiye Bahcesehir Univ Fac Engn & Nat Sci Istanbul Turkiye Piri Reis Univ Fac Sci & Letters Istanbul Turkiye Shabihsazan Ati Pars Fast Comp Ctr Tehran Iran

This study looked at ANNs' ability to predict the rheological properties of MWCNT-ZNO / Oil SAE 50 nano lubricant. Five artificial intelligence algorithms-Group Method of Data Handling (GMDH), Extreme Gradient Boosting (XGBoost), Multivariate Adaptive Regression Splines (MARS), Support vector machine (SVM), and Multilayer Perceptron (MLP)-were employed in this work to forecast this nanofluid. The most optimum objective function (mu nf) as an output is the foundation of algorithms used in artificial intelligence. This capacity is developed so that the values predicted by ANN were more consistent with the laboratory numbers by combining GMDH with the metaheuristic approach. This combination enables the metaheuristic algorithm to optimize the evaluation indices and get the predicted data closer to the experimental data by using the GMDH activation parameters as input. For optimization, three metaheuristic algorithms are used, and the combination of GMDH and MOGWO produced the best results. Ultimately, the finest condition that could be achieved is found to have the following input data values: share rate (gamma), temperature (T), and solid volume fraction (phi): 0.0625 %, 50 degrees C, and 5499.6783 s-1 correspondingly.

关键词： Nano-lubricant Meta-heuristic artificial intelligence algorithms Metaheuristic algorithm

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A Review of Reliability Assessment and Lifetime Prediction Methods for Electrical Machine Insulation Under Thermal Aging

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ENERGIES 2025年第3期18卷 576-576页

作者： Zhang, Jian Wang, Jiajin Li, Hongbo Zhang, Qin He, Xiangning Meng, Cui Huang, Xiaoyan Fang, Youtong Wu, Jianwei Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Zhejiang Guoli Secur Technol Co Ltd Hangzhou 310059 Peoples R China Zhejiang Univ Sch Mech Engn Hangzhou 310027 Peoples R China

The thermal aging of insulation systems in electrical machines is a critical factor influencing their reliability and lifetime, particularly in modern high-performance electrical equipment. However, evaluating and predicting insulation lifetime under thermal aging poses significant challenges due to the complex aging mechanisms. Thermal aging not only leads to the degradation of macroscopic properties such as dielectric strength and breakdown voltage but also causes progressive changes in the microstructure, making the correlation between aging stress and aging indicators fundamental for lifetime evaluation and prediction. This review first summarizes the performance indicators reflecting insulation thermal aging. Subsequently, it systematically reviews current methods for reliability assessment and lifetime prediction in the thermal aging process of electrical machine insulation, with a focus on the application of different modeling approaches such as physics-of-failure (PoF) models, data-driven models, and stochastic process models in insulation lifetime modeling. The theoretical foundations, modeling processes, advantages, and limitations of each method are discussed. In particular, PoF-based models provide an in-depth understanding of degradation mechanisms to predict lifetime, but the major challenge remains in dealing with complex failure mechanisms that are not well understood. Data-driven methods, such as artificial intelligence or curve-fitting techniques, offer precise predictions of complex nonlinear relationships. However, their dependence on high-quality data and the lack of interpretability remain limiting factors. Stochastic process models, based on Wiener or Gamma processes, exhibit clear advantages in addressing the randomness and uncertainty in degradation processes, but their applicability in real-world complex operating conditions requires further research and validation. Furthermore, the potential applications of thermal lifetime models, such as

关键词： thermal aging physics of failure artificial intelligence algorithms curve-fitting technologies stochastic process application of insulation lifetime model

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Spillovers, Bottlenecks, and More Invention After Invention

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IEEE MICRO 2025年第1期45卷 101-103页

作者： Greenstein, Shane Harvard Sch Business Boston MA 02163 USA

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Urban flood management from the lens of social media data using machine learning algorithms

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JOURNAL OF HYDROLOGY 2025年 653卷

作者： Boota, Muhammad Waseem Soomro, Shan-e-hyder Xu, Jikun Ahmad, Muhammad Irshad Xia, Haoming Qin, Yaochen Idrees, Muhammad Bilal Ahmad, Naseem Yan, Chaode Yousaf, Ayesha Ning, Xiaoju Henan Univ Coll Geog & Environm Sci Kaifeng 475004 Peoples R China Henan Univ Henan Key Lab Earth Syst Observat & Modeling Kaifeng 475004 Peoples R China Henan Univ Minist Educ Key Lab Geospatial Technol Middle & Lower Yellow R Kaifeng 475004 Peoples R China Henan Univ Key Res Inst Yellow River Civilizat & Sustainable Kaifeng 475004 Peoples R China Henan Univ Collaborat Innovat Ctr Yellow River Civilizat Join Kaifeng 475004 Peoples R China China Three Gorges Univ Coll Hydraul & Environm Engn Yichang 443002 Peoples R China Zhengzhou Univ Sch Water Conservancy & Transportat Zhengzhou 450001 Peoples R China Zhengzhou Univ Business Sch Zhengzhou 450001 Peoples R China Dalian Univ Technol Fac Infrastruct Engn Dalian 116024 Peoples R China Univ Peshawar Natl Ctr Excellence Geol Peshawar 25000 Pakistan Henan Univ Technol Coll Mech & Elect Engn Zhengzhou 450052 Peoples R China Henan Univ Econ & Law Sch Resources & Environm Zhengzhou 450046 Peoples R China

Urban flooding (UFL) occurs frequently due to climatic variations and anthropogenic activities. Real-time information on the physical and emotional damage caused by UFL is critical for catastrophic emergency management, but methods to rapidly acquire such information are lacking. Social media promptly disseminates emergency information and has become a valuable source for real-time data with low acquisition cost and rich content. Here, we present a framework to rapidly categorize physical and emotional damage caused by UFL events using data scraped from Twitter and Facebook and machine-learning classification algorithms. To test our framework, we use a UFL event from August 2022 in the Swat Valley of Pakistan. First, we obtain data from Twitter and Facebook users worldwide. Second, we filter the data using deduplication and denoising. Last, we use a novel approach based on machine-learning algorithms to assess the physical and emotional damage caused by the flooding. We also explore the capacity for recovery by analyzing keyword frequencies indicative of floodinduced destruction. We observe that social media can be used to assess the extent of damage and the progress of the recovery process. We find that people in regions with more physical damage tend to be more emotionally affected. Also, government officials tend to disseminate positive information to mitigate public panic during flooding episodes, and students are more likely to express negative emotions. Our UFL disaster classification framework captures changes in the physical and emotional state of UFL-stricken areas in real time and can be leveraged by emergency responders during disaster management and recovery efforts and by managers to mitigate the impacts of future potential UFL disasters.

关键词： Urban floods Spatio-temporal variations Physical and emotional damage Empowerment artificial intelligence algorithms

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Photonic platform coupled with machine learning algorithms to detect pyrolysis products of crack cocaine in saliva: A proof-of-concept animal study

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2025年 329卷 125635页

作者： Santana-Melo, Igor Caixeta, Douglas Carvalho Aguiar, Emilia Maria Gomes Cardoso-Sousa, Leia Pacheco, Amanda Larissa Dias dos Santos, Yngrid Mickaelli Oliveira da Silva, Jefte Teixeira Santana, Antonio Euzebio Goulart Carneiro, Murillo Guimaraes de Castro, Olagide Wagner Sabino-Silva, Robinson Fed Univ Alagoas UFAL Inst Biol Sci & Hlth Ave Lourival de Melo Motakm 14Campus AC SimoesC BR-57072970 Maceio AL Brazil Fed Univ Uberlandia UFU Inst Biomed Sci ICBIM Innovat Ctr Salivary Diag & Nanotheranost Dept Physiol Ave Univ Fed Alagoas Campus Engn & Agrarian Sci Maceio AL Brazil Univ Fed Uberlandia Fac Comp Uberlandia MG Brazil

The non-invasive detection of crack/cocaine and other bioactive compounds from its pyrolysis in saliva can provide an alternative for drug analysis in forensic toxicology. Therefore, a highly sensitive, fast, reagent-free, and sustainable approach with a non-invasive specimen is relevant in public health. In this animal model study, we evaluated the effects of exposure to smoke crack cocaine on salivary flow, salivary gland weight, and salivary composition using Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy. The exposure to crack cocaine was performed in an acrylic box apparatus with a burned activation of crack/cocaine 400 mg for 10 min for 14 consecutive days. Crack/cocaine exposure increased the salivary secretion without changes in parotid and submandibular weights. Hierarchical Clustering Analysis (HCA) was applied to depict subgrouping patterns in infrared spectra, and Principal components analysis (PCA) explained 83.2 % of the cumulative variance using 3 PCs. ATR-FTIR platforms were coupled to AdaBoost, artificial Neural Networks, Na & iuml;ve Bayes, Random Forest, and Support Vector Machine (SVM) algorithms tool to identify changes in the infrared salivary spectra of rats exposed to crack cocaine. The best classification of crack cocaine exposure using the salivary spectra was performed by Na & iuml;ve Bayes, presenting a sensitivity of 100 %, specificity of 80 %, and accuracy of 90 % between crack cocaine and control rats. The SHAP features of salivary infrared spectra mostly indicate the vibrational modes at 1331 cm- 1 and 2806 cm-1 , representing CH2 wagging commonly linked in lipids and C-H stretch often attributed to the CH2 or CH3 groups in lipid molecules, respectively, as the main responsible vibrational modes for crack cocaine exposure discrimination. In summary, the present pre-clinical findings indicate the potential of the ATR-FTIR platform coupled with machine learning to effectively detect changes in salivary in

关键词： Crack cocaine Flow salivary Screening tool ATR-FTIR artificial intelligence algorithms

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Machine as a smart service: a hybrid knowledge graph approach

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2024年 1-26页

作者： Ren, Huanrong Jiang, Pingyu Li, Qingzong Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

The advancement of Information and Communication Technology has catalyzed the emergence of a new generation of manufacturing paradigms demanding increasingly "smart" equipment. Despite this, existing equipment system exhibits limitations in terms of flexibility, user interaction, and cost-effectiveness. To address these challenges and foster personalization, smartness, and a service-oriented approach, this article proposed an innovative smart equipment service system implementation of "Machine as a smart service" (MaaS). We argue that shifting the past concentration from the equipment itself to the service provided would fitting for the current advanced manufacturing paradigms more properly, and based on this proposed a hybrid Knowledge Graph (KG) approach to abstract equipment as smart services. Our MaaS is running on a cluster consisting of an arbitrary quantity of equipment and makes H2M/M2H/M2M communication through service-oriented interaction. The process and related resources would be mapped into a twin space organized with KG, which translates the MaaS physical running into KG interaction. We also introduced three enabling technologies to achieve this: Hierarchical Knowledge Graph, Event Control Framework, and resource mapping methods, and illustrate them with cases.

关键词： Smart equipment Distributed system Service-oriented Knowledge graph Smart interface artificial intelligence algorithms

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