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Multi-Beam Beamforming-Based ml algorithm to Optimize the Routing of Drone Swarms

DRONES 2024年第2期8卷

作者： Myers, Rodman J. Perera, Sirani M. Mclewee, Grace Huang, David Song, Houbing Florida Southern Coll Dept Comp Sci Lakeland FL 33801 USA Embry Riddle Aeronaut Univ Dept Math Daytona Beach FL 32114 USA Coastal Carolina Univ Dept Comp Sci Conway SC 29528 USA Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA Univ Maryland Baltimore Cty Dept Informat Syst Baltimore MD 21250 USA

The advancement of wireless networking has significantly enhanced beamforming capabilities in Autonomous Unmanned Aerial Systems (AUAS). This paper presents a simple and efficient classical algorithm to route a collection of AUAS or drone swarms extending our previous work on AUAS. The algorithm is based on the sparse factorization of frequency Vandermonde matrices that correspond to each drone, and its entries are determined through spatiotemporal data of drones in the AUAS. The algorithm relies on multibeam beamforming, making it suitable for large-scale AUAS networking in wireless communications. We show a reduction in the arithmetic and time complexities of the algorithm through theoretical and numerical results. Finally, we also present an ml-based AUAS routing algorithm using the classical AUAS algorithm and feed-forward neural networks. We compare the beamformed signals of the ml-based AUAS routing algorithm with the ground truth signals to minimize the error between them. The numerical error results show that the ml-based AUAS routing algorithm enhances the accuracy of the routing. This error, along with the numerical and theoretical results for over 100 drones, provides the basis for the scalability of the proposed ml-based AUAS algorithms for large-scale deployments.

关键词： drone swarms UAS MIMO communication complexity and performance of algorithms wireless communications multi-beam beamforming ml algorithm ANN

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Dual port ring cylindrical dielectric resonator antenna optimization using ml algorithm

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WAVES IN RANDOM AND COMPLEX MEDIA 2022年

作者： Mishra, Shivam Maurya, Shubahm Das, Yashbardhan Kumar, Vinay Ranjan, Pinku Gupta, Harshit Pandey, Ashish Sharma, Anand MNNIT Allahabad Dept Elect & Commun Engn Prayagraj India Indian Inst Informat Technol & Management IIITM ABV Gwalior India NIT Meghalaya Shillong India

In the present scenario, Machine Learning techniques are used in much ongoing research as a powerful tool. This paper proposes the applications of machine learning in antenna design optimization by implementing different machine learning algorithms like KNN, ANN, Random Forest, XGBoost and Decision Tree. A Double ring Cylindrical Di-electric Resonator Antenna is designed using High-Frequency Structure Simulator (HFSS). For the proposed antenna design, the frequency range is 2-3.5 GHz, while the range of height and radius is 6.5-19.5 mm and 12-18 mm respectively. The data set is generated for the proposed antenna design and S-11 parameter is optimized using machine learning algorithms. Out of the five algorithms, the models for KNN, XGBoost and Artificial Neural Network perform almost similarly and Random Forest has the highest performance.

关键词： Antenna double ring CDRA HFSS ml algorithm KNN ANN

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Determination of the surface roller length of hydraulic jumps in horizontal rectangular channels using the machine learning method

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2024年第7期38卷 2539-2562页

作者： Ho, Hung Viet Thuyloi Univ Fac Water Resources Engn 175 Tay Son Hanoi 116705 Vietnam

This study aims to develop and assess seven machine learning (ml) models, including an Artificial Neural Network (ANN), Gradient Boosting (GB), Extra Trees (ET), Lasso Regression (LASSO), Least Angle Regression (Lars), Random Forest (RF), and Adaptive Boosting (Adaboost), that are effective and simple in forecasting the roller length of a hydraulic jump in a rectangular horizontal canal. This study also proposes a novel approach for applying the Buckingham Pi theorem to ascertain four model inputs and one output, corresponding to five dimensionless Pi terms that have not previously been explored. The impact of channel surface roughness, fluid viscosity, channel geometry, and gravity on the hydraulic jump's roller length was considered to improve the model's performance. Furthermore, the importance of input features was determined, resulting in two model input scenarios. The scenario with four inputs containing the inflow Reynolds number Re1* surpasses the three-input scenario. Because of their high accuracy and efficiency, six ml models outperformed three empirical equations. The GB, ANN, and ET models showed the best performance in both validation and testing phases, whereas LASSO, Lars, RF, and Adaboost performed with lower precision, in descending order. Regarding the ANN model, the best has two hidden layers of sixteen neurons each. In the validation and testing phases, this model achieves the highest Nash-Sutcliffe efficiency of 0.992 and 0.933, respectively. As a result, this study recommends using the GB, ANN, and ET models to forecast the roller length of the hydraulic jump instead of empirical equations when the inflow Froude number, Fr1, varies between 2.4 and 15.9.

关键词： Hydraulic jump ml algorithm ANN Buckingham theorem Roller length

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Exploring the fresh and rheology properties of 3D printed concrete with fiber reinforced composites (3DP-FRC): a novel approach using machine learning techniques

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MATERIALS RESEARCH EXPRESS 2024年第12期11卷 125502-125502页

作者： Rasel, Risul Islam Hossain, Md Minaz Zubayer, Md Hasib Zhang, Chaoqun Tongji Univ Coll Civil Engn Dept Bridge Engn Shanghai Peoples R China Tongji Univ Coll Civil Engn Dept Disaster Mitigat Struct Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China Sun Yat Sen Univ Sch Mat Shenzhen 518107 Peoples R China

This study focuses on the prediction models for four parameters related to the fresh and rheological properties of 3DP-FRC: spreading diameters (SPD), dynamic yield stress (DYs), static yield stress (SYs) and plastic viscosity (PV), respectively. Five machine learning (ml) algorithms were employed, namely artificial neural network (ANN), random forest (RF), decision tree (DT), categorical boosting (CatBoost), and extreme gradient boosting (XGBoost). An extensive dataset was compiled including 373 (SPD) and 219 (SYs, DYs, PV) from various literature comprising experimental results. Fifteen input parameters were identified as the most influential factors affecting the fresh and rheological properties. These parameters include OPC, W/B, W/S, FA, LP, SF, SP, VMA, W, hf, Ri, AR, tsf, Ft, and Stime/Rtime. This study found strong correlations between the developed ml models and the experimental outcomes from both the training and testing datasets. The models demonstrated exceptional accuracy and provided precise predictions for SPD, SYs, DYs, and PV. The correlation coefficients (R2) ranged from 0.94 to 0.99 for SPD, 0.93 to 0.99 for SYs, 0.98 to 0.99 for DYs, and 0.98 to 1.00 for PV, with consistent results observed across both the training and testing datasets. Moreover, the model's precision was assessed using different error metrics, including root mean square error (RMSE), mean square error (MSE), coefficient of variation in root-mean-square error (CVRMSE), and mean absolute error (MAE). Sensitivity analysis was performed to identify their impact. Additionally, fiber dependent analysis was conducted to assess the effectiveness of different fiber types on the fresh and rheological properties (SPD, SYs, DYs, and PV). In conclusion, the ml models were effectively trained and optimized, resulting in accurate and highly predictive capabilities for the parameters of interest.

关键词： fresh and rheology properties 3D printing concrete with fiber-based (3DP-FRC) ml algorithm fiber-type depended analysis sensitivity analysis

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Electromechanical vibrations of smart piezoelectric structures on auxetic foundations for roadway support: a machine learning-enhanced CUF approach

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2025年

作者： Yang, Yiran Du, Yunhang Li, Jianying Lu, Zidong Li, Hongwei Wu, Jiangjie Yuan, Kekuo Yunnan Vocat Inst Energy Technol Sch Resources & Environm Engn Qujing Yunnan Peoples R China Yunnan Vocat Inst Energy Technol Sch Mech & Elect Engn Qujing Yunnan Peoples R China Xijing Univ Sch Civil Engn Xian Shaanxi Peoples R China

This study presents a comprehensive analysis of electromechanical vibrations in smart piezoelectric structures resting on auxetic foundations, intended for roadway support applications. The mathematical simulation is developed using the Carrera unified formulation (CUF), which enables the implementation of refined, higher-order structural models within a unified theoretical framework. CUF allows for the accurate representation of coupled mechanical and electrical fields in piezoelectric materials, while also capturing the complex deformation behavior introduced by auxetic substrates. The simulation accounts for dynamic loading conditions relevant to transportation infrastructure and evaluates critical parameters such as displacement, stress distribution, and electric potential. To validate and complement the numerical results, a machine learning (ml) algorithm is designed and trained using the dataset generated from the CUF-based simulations. The trained ml algorithm demonstrates strong predictive accuracy and computational efficiency, successfully replicating the nonlinear electromechanical behavior of the system. This hybrid methodology not only verifies the mathematical model but also offers a powerful, data-driven alternative for real-time response prediction and structural health monitoring. The integration of CUF and machine learning provides a scalable and robust platform for designing intelligent roadway support systems. The findings underscore the potential of combining advanced mechanics formulations with artificial intelligence to enhance the performance, reliability, and adaptability of next-generation civil infrastructure.

关键词： CUF approach electromechanical vibrations ml algorithm smart piezoelectric structures auxetic foundation roadway support

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Analysis of demand forecasting of agriculture using machine learning algorithm

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2024年第1期26卷 1731-1747页

作者： Chelliah, Balika J. Latchoumi, T. P. Senthilselvi, A. SRM Inst Sci & Technol Dept Comp Sci & Engn Ramapuram Campus Chennai 600089 India

The state of India was situated on fertile land and river deltas with appropriate agricultural land. In 2019, agricultural fields, primarily cropland, occupy more than 40% of the country's total surface area. Moreover, agricultural industry revenues less than 3% of the country's Gross Provincial Product (GPP). While manufacturing has become the country's major financial activity, accounting countries represent half of GPP's revenues. The objective of the study is to find a way to improve the financial profitability and efficiency of farming supply chain networks as follows: (1) Fixation of national-level targets for zonal-level groups information after assessment and forecasts that affect production and distribution of agriculture. (2) Producers risk can be reduced by directing people to multiple factory and industrialization options based on market assessment. (3) Insurance costs and reduction of bank borrowing by standardizing the connection between bankers and producers using the structure to centralise land information. (4), Stabilize the agricultural sector by looking at nearby potential destinations for production and regulation of the Public Distribution System (PDS) flow for the security stock. In this paper, the novel ml target prediction algorithm to inform the farmers about the market target product and improve the relationships between the farmer and bankers for centralizing the information about recent government plans. The crop prediction ml algorithm proposed to improve the revenue of agriculture field.

关键词： Crop rotation ml algorithm Farming sector Crop cultivation Tracking and Forecasting

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Detection of ChatGPT fake science with the xFakeSci learning algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Hamed, Ahmed Abdeen Wu, Xindong SUNY Binghamton Complex Adapt Syst & Computat Intelligence Lab Binghamton NY 13902 USA Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ China Hefei 230009 Peoples R China China Elect Data Ind Grp Shenzhen 518057 Peoples R China

Generative AI tools exemplified by ChatGPT are becoming a new reality. This study is motivated by the premise that "AI generated content may exhibit a distinctive behavior that can be separated from scientific articles". In this study, we show how articles can be generated using means of prompt engineering for various diseases and conditions. We then show how we tested this premise in two phases and prove its validity. Subsequently, we introduce xFakeSci, a novel learning algorithm, that is capable of distinguishing ChatGPT-generated articles from publications produced by scientists. The algorithm is trained using network models driven from both sources. To mitigate overfitting issues, we incorporated a calibration step that is built upon data-driven heuristics, including proximity and ratios. Specifically, from a total of a 3952 fake articles for three different medical conditions, the algorithm was trained using only 100 articles, but calibrated using folds of 100 articles. As for the classification step, it was performed using 300 articles per condition. The actual label steps took place against an equal mix of 50 generated articles and 50 authentic PubMed abstracts. The testing also spanned publication periods from 2010 to 2024 and encompassed research on three distinct diseases: cancer, depression, and Alzheimer's. Further, we evaluated the accuracy of the xFakeSci algorithm against some of the classical data mining algorithms (e.g., Support Vector Machines, Regression, and Naive Bayes). The xFakeSci algorithm achieved F1 scores ranging from 80 to 94%, outperforming common data mining algorithms, which scored F1 values between 38 and 52%. We attribute the noticeable difference to the introduction of calibration and a proximity distance heuristic, which underscores this promising performance. Indeed, the prediction of fake science generated by ChatGPT presents a considerable challenge. Nonetheless, the introduction of the xFakeSci algorithm is a significant step

关键词： ChatGPT Generative AI Fake publication Human-generated publications ml algorithm Fake science

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Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO2 with periodic table descriptors using machine learning approaches

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BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2023年 14卷 939-950页

作者： Roy, Joyita Pore, Souvik Roy, Kunal Jadavpur Univ Dept Pharmaceut Technol Drug Theoret & Cheminformat Lab Kolkata 700032 India

Nanoparticles with their unique features have attracted researchers over the past decades. Heavy metals, upon release and emission, may interact with different environmental components, which may lead to co-exposure to living organisms. Nanoscale titanium dioxide (nano-TiO2) can adsorb heavy metals. The current idea is that nanoparticles (NPs) may act as carriers and facilitate the entry of heavy metals into organisms. Thus, the present study reports nanoscale quantitative structure-activity relationship (nano-QSAR) models, which are based on an ensemble learning approach, for predicting the cytotoxicity of heavy metals adsorbed on nano-TiO2 to human renal cortex proximal tubule epithelial (HK-2) cells. The ensemble learning approach implements gradient boosting and bagging algorithms;that is, random forest, AdaBoost, Gradient Boost, and Extreme Gradient Boost were constructed and utilized to establish statistically significant relationships between the structural properties of NPs and the cause of cytotoxicity. To demonstrate the predictive ability of the developed nano-QSAR models, simple periodic table descriptors requiring low compu-tational resources were utilized. The nano-QSAR models generated good R2 values (0.99-0.89), Q2 values (0.64-0.77), and Q2F1 values (0.99-0.71). Thus, the present work manifests that ml in conjunction with periodic table descriptors can be used to explore the features and predict unknown compounds with similar properties.

关键词： heavy metals HK-2 cell ml algorithm periodic table descriptors QSAR

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A comprehensive evaluation of liposome/water partition coefficient prediction models based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method: Challenges from different descriptor dimension reduction methods and machine learning algorithms

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JOURNAL OF HAZARDOUS MATERIALS 2023年第PT A期443卷 130181页

作者： Zhu, Tengyi Yu, Yan Tao, Tianyun Yangzhou Univ Sch Environm Sci & Engn Yangzhou 225127 Jiangsu Peoples R China Yangzhou Univ Coll Agr Yangzhou 225009 Jiangsu Peoples R China

The liposome/water partition coefficient (Klip/w) is a key parameter to evaluate the bioaccumulation potential of pollutants. Considering that it is difficult to determine the Klip/w values of all pollutants through experiments, researchers gradually developed models to predict it. However, there is currently no research on how to comprehensively evaluate prediction models and recommend a compelling optimal modeling method. To remedy the defect of single parameters in a traditional model comparison, the TOPSIS evaluation method, based on entropy weight, was first proposed. We use this method to comprehensively evaluate models from multiple angles in this study. Thirty QSPR models, including 3 descriptor dimension reduction methods and 10 algorithms (belonging to 4 tribes), were used to predict Klip/w and verify the effectiveness of the comprehensive assessment method. The results showed that RF (descriptor dimension reduction method), symbolism (tribes) and RF (algorithm) exhibited significant advantages in establishing the Klip/w value prediction model. At present, the application of TOPSIS in environmental model evaluations is almost absent. We hope that the proposed TOPSIS evaluation method can be applied to more chemical datasets and provide a more systematic and comprehensive basis for the application of the QSPR model in environmental studies and other fields.

关键词： Liposome water partition coefficient QSPR models ml algorithm algorithm Descriptor dimension reduction method Comprehensive evaluation method

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AI Driven Medical Prediction and Suggestive System to Establish Good Health and Well Being in Society - A Real Time Case Study

AI Driven Medical Prediction and Suggestive System to Establ...

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International Conference on Intelligent and Fuzzy Systems (INFUS)

作者： Verma, Sunita Jain, Anjana Shukla, Sangeeta Kothari, Vineeta Shri GS Inst Technol & Sci Indore India SAGE Univ Indore Indore India Cent Pathol Lab Indore India

ISBN: (纸本)9783031671913;9783031671920

To Achieve one of the goal of United Nation for 2030 to Establish good health & well being for sustainable development, various steps are being taken globally. To achieve the objectives of good health, monitoring and prediction based suggestive system is required specific to specific class. The emergence of Artificial Intelligent techniques and algorithms may play a vital role in fast, reliable and accurate data analysis and prediction. Further the echo system may be developed and policies may be reframed to achieve the UN goal 2030. In this paper a case study is presented, which is carried out in the host city Indore situated in central India. The city has uniform climate and living style across it's boundary & inner part. Hence the data taken can be categorized under one class. The sampling has been done in real time for the Medical Parameters like B Glucose, Cholesterol, Keratinize and Protein. Different subclasses were created for recognizing pattern of specific class. The classification is done on urban and Rural population, on Gender basis, on caste and age wise. Almost ten subclasses are described which are having unique and distinguishable characteristics in their food style. Present Data is compared with data set of interval of 10 years. With the use of AI algorithm the estimation on medical health has been predicted while considering different parameters. These algorithms generate functions that map inputs to desire outputs and consist of a dependent variable which is predicted from a given set of independent variables. The predicted values are compared with the original value to determine the accuracy of the model. The machine learning algorithm include Naive-Bayes, k-NN, Decision Tree, Support Vector Machines, Random Forest, bagging etc. The results are derived formentioned subclasses and specific groups. Based on the analysis and prediction a specific suggestive health care system can be developed for specific class or subclass. The relevant society may

关键词： UN 2030 Artificial Intelligent techniques Health care Lifestyle Predictive analysis ml algorithm

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