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Identification of rural courtyards’utilization status using deep learning and machine learning methods on unmanned aerial vehicle images in north China

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Building Simulation 2024年第5期17卷 799-818页

作者： Maojun Wang Wenyu Xu Guangzhong Cao Tao Liu College of Resource Environment and Tourism Capital Normal UniversityBeijing 100048China College of Urban and Environmental Sciences Peking UniversityBeijing 100871China

The issue of unoccupied or abandoned homesteads(courtyards)in China emerges given the increasing aging population,rapid urbanization and massive rural-urban *** the aspect of rural vitalization,land-use planning,and policy making,determining the number of unoccupied courtyards is *** and questionnaire-based surveys were currently the main approaches,but these traditional methods were often expensive and laborious.A new workflow is explored using deep learning and machine learning algorithms on unmanned aerial vehicle(UAV)***,features of the built environment were extracted using deep learning to evaluate the courtyard management,including extracting complete or collapsed farmhouses by Alexnet,detecting solar water heaters by YOLOv5s,calculating green looking ratio(GLR)by *** precisions exceeded 98%.Then,seven machine learning algorithms(Adaboost,binomial logistic regression,neural network,random forest,support vector machine,decision trees,and XGBoost algorithms)were applied to identify the rural courtyards’utilization *** Adaboost algorithm showed the best performance with the comprehensive consideration of most metrics(Accuracy:0.933,Precision:0.932,Recall:0.984,F1-score:0.957).Results showed that identifying the courtyards’utilization statuses based on the courtyard built environment is *** is transferable and cost-effective for large-scale village surveys,and may contribute to the intensive and sustainable approach to rural land use.

关键词： unoccupied homesteads courtyard built environment inner courtyard management GLR solar water heaters machine learning algorithms

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Performance Comparison of machine learning algorithms for Diagnosis of Cardiotocograms with Class Inequality 22

Performance Comparison of Machine Learning Algorithms for Di...

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22nd Telecommunications Forum Telfor (TELFOR)

作者： Stylios, Ioannis Chr. Vlachos, Vasileios Androulidakis, Iosif Univ Athens Dept Informat & Telecommun Athens 15784 Greece Technol Educ Inst Thessaly Dept Comp Sci & Engn Thessaly Greece Univ Ioannina GR-45110 Ioannina Greece

ISBN: (纸本)9781479961917

The objective of the present paper is to demonstrate the potential of Computational Intelligence in applications pertaining to the automatic identification categorisation of Cardiotocograms using machine learning algorithms and Artificial Neural Networks whose purpose is to distinguish between healthy or pathological cases leading to mortality during birth or fetal cerebral palsy. Interest is also placed on the performance of the machine learning algorithms and the comparison of the classifiers' results.

关键词： Artificial Neural Networks Cardiotocograms machine learning algorithms WEKA

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machine learning-Based Monitoring Trail: On Fast and Accurate Optical Path Failure Localization in All-Optical DCNs

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第5期43卷 2039-2052页

作者： Zhao, Zhipeng Guo, Lei Wu, Bin Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Peoples R China Chongqing Univ Posts & Telecommun Inst Intelligent Commun & Network Secur Chongqing 400065 Peoples R China Henan Univ Technol Minister Educ Key Lab Grain Informat Proc & Control Zhengzhou 450001 Peoples R China Tianjin Univ Coll Intelligence & Comp Sch Comp Sci & Technol Tianjin 300072 Peoples R China

We propose machine learning-based Monitoring Trail (mlm-trail) to monitor optical path failures for large model training. Existing monitoring trail (m-trail) method can only localize optical link failures, whereas mlm-trail differs from it by combining with machine learning and providing bidirectional voltage constraint. mlm-trail can provide fast, accurate and integrated failure localization of both optical links and optical switches using only a small number of monitors. Firstly, we construct an input dataset based on the edge relationships of 10000 virtual network topologies (the mappings of optical links and optical switches in all-optical data center networks (DCNs)). Then, monitoring trail under bidirectional voltage constraint is formulated by integer liner program (ILP) to minimize the overall monitoring cost (including monitor and bandwidth costs), and thus construct an output dataset. Finally, we train the learning model based on above dataset using classical and proposed hybrid machine learning models to achieve fast generation of monitoring trails. Based on the constraints of full coverage of monitoring trail and uniqueness of alarms, the generated output results based on machine learning are modified to achieve unambiguous localization for each optical path. Numerical results show that mlm-trail outperforms m-trail in localization speed and scalability, and also outperforms machine learning algorithms in accuracy and cost.

关键词： Monitoring Optical switches Location awareness Accuracy machine learning Integrated optics Costs Training Predictive models machine learning algorithms All-optical data center networks (DCNs) integer liner program (ILP) optical path failure localization machine learning-based on monitoring trail (mlm-trail)

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machine learning Approaches to Code Similarity Measurement: A Systematic Review

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IEEE ACCESS 2025年 13卷 51729-51764页

作者： Zhang, Zixian Saber, Takfarinas Univ Galway Sch Comp Sci CRT AI Galway H91TK33 Ireland Univ Galway Sch Comp Sci Lero Galway H91TK33 Ireland

Source code similarity measurement, which involves assessing the degree of difference between code segments, plays a crucial role in various aspects of the software development cycle. These include but are not limited to code quality assurance, code review processes, code plagiarism detection, security, and vulnerability analysis. Despite the increasing application of ML technique in this domain, a comprehensive synthesis of existing methodologies remains lacking. This paper presents a systematic review of machine learning techniques applied to code similarity measurement, aiming to illuminate current methodologies and contribute valuable insights to the research community. Following a rigorous systematic review protocol, we identified and analyzed 84 primary studies on a broad spectrum of dimensions covering application type, devised machine learning algorithms, used code representations, datasets, and performance metrics, as well as performance evaluations. A deep investigation reveals that 15 applications for code similarity measurement have utilized 51 different machine learning algorithms. Additionally, the most prevalent code representation is found to be the abstract syntax tree (AST). Furthermore, the most frequently employed dataset across various code similarity research applications is BigCloneBench. Through this comprehensive analysis, the paper not only synthesizes existing research but also identifies prevailing limitations and challenges, shedding light on potential avenues for future work.

关键词： Codes Cloning Systematic literature review Syntactics Plagiarism Semantics machine learning Unsupervised learning Source coding machine learning algorithms Code similarity code clone machine learning deep learning systematic literature review

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Detecting and profiling sedentary young men using machine learning algorithms 5

Detecting and profiling sedentary young men using machine le...

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Siirtola, Pekka Pyky, Riitta Ahola, Riikka Koskimaki, Heli Jamsa, Timo Korpelainen, Raija Roning, Juha Univ Oulu Comp Sci & Engn Dept POB 4500 FI-90014 Oulu Finland Oulu Deaconess Inst Dept Sports & Exercise Med Oulu Finland Oulu Univ Hosp Med Res Ctr Oulu Oulu Finland Univ Oulu SF-90100 Oulu Finland Univ Oulu Fac Med Inst Hlth Sci Oulu Finland Univ Oulu Fac Med Dept Med Technol SF-90100 Oulu Finland

ISBN: (纸本)9781479945184

Many governments and institutions have guidelines for health-enhancing physical activity. Additionally, according to recent studies, the amount of time spent on sitting is a highly important determinant of health and wellbeing. In fact, sedentary lifestyle can lead to many diseases and, what is more, it is even found to be associated with increased mortality. In this study, a data set consisting of self-reported questionnaire, medical diagnoses and fitness tests was studied to detect sedentary young men from a large population and to create a profile of a sedentary person. The data set was collected from 595 young men and contained altogether 678 features. Most of these are answers to multi-choice close-ended questions. More precisely, features were mostly integers with a scale from 1 to 5 or from 1 to 2, and therefore, there was only a little variability in the values of features. In order to detect and profile a sedentary young man, machine learning algorithms were applied to the data set. The performance of five algorithms is compared (quadratic discriminant analysis (QDA), linear discriminant analysis (LDA), C4.5, random forests, and k nearest neighbours (kNN)) to find the most accurate algorithm. The results of this study show that when the aim is to detect a sedentary person based on medical records and fitness tests, LDA performs better than the other algorithms, but still the accuracy is not high. In the second part of the study the differences between highly sedentary and non-sedentary young men are searched, recognition can be obtained with high accuracy with each algorithm.

关键词： learning (artificial intelligence) medical diagnostic computing fitness test health-enhancing physical activity machine learning algorithms medical diagnosis sedentary young men Accuracy Diseases Educational institutions Feature extraction machine learning algorithms Medical diagnostic imaging Sociology machine learning algorithms medical diagnostic computing Medical diagnosis Sociology Educational institutions Feature extraction Fitness Testing maladjustment health diagnostic imaging Disease diagnosis

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machine learning techniques for predicting the adsorption capacity of Synergistic biochar Functionalization with Pyrrole-Sulfanilic acid copolymer in mercury and chromium remediation

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CHEMICAL ENGINEERING JOURNAL 2025年 503卷

作者： Fekry, Nesma A. Mahmoud, Mohamed E. Kamel, Nesma K. Amira, Mohamed F. Alexandria Univ Fac Sci Chem Dept Alexandria Egypt

A novel and sustainable nanobiosorbent was developed via immobilization of N-doped-biochar onto co-polymer matrix, specifically sulfanilic acid with pyrrole in presence of formaldehyde as a crosslinker. Firstly, N-dopedbiochar was synthesized from pumpkin seeds in the presence of KHCO3 as an activator and urea as a source of nitrogen. This product was reacted with pyrrole, sulfanilic acid as monomers and formaldehyde to generate NPSB@Co-Poly(SU-For-Py) and thoroughly characterized using Fourier Transformation Infrared (FT-IR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive X-ray (EDX), X-ray diffraction (XRD), X-ray photoelectron (XPS), and thermogravimetric analysis (TGA), confirming its structure morphology and elemental composition. N-PSB@Co-Poly(SU-For-Py) demonstrated significant efficacy in the removal processes of Cr(VI) (99.63 % at pH 2) and Hg(II) (95.66 % at pH 3) from aqueous solutions. The experimental results aligned with the pseudo-second-order kinetic model (R-2 = 0.991 and 0.999 for Cr(VI) and Hg(II), respectively) and established equilibrium condition after 50 min contact time for both contaminants. This research also highlights the successful recycling of the nanobiosorbent over 5 cycles, maintaining a commendable >50 % efficiency. In addition, the use of artificial intelligence (AI) models to investigate and evaluate the sorption efficiency of Cr (VI) and Hg(II) onto N-PSB@Co-Poly(SU-For-Py) was also explored. Accordingly, the two machine learning (ML) algorithms used in this prediction are based on the support vector regression (SVR) and random forest (RF). The SVR algorithm was qualified utilizing three kernels: Pearson VII universal kernel (PUK), radial basis function (RBF), and polynomial kernel (PolyK). The optimization of the algorithms was based on (i) the correlation coefficient (R) and (ii) several error functions (mean absolute error, root mean square error, Relative absolute error, and root

关键词： Copolymer (Polypyrrole/sulfanilic acid) N -doped biochar Chromium Mercury machine learning algorithms

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Improving Sleep Disorder Diagnosis Through Optimized machine learning Approaches

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IEEE ACCESS 2025年 13卷 20989-21004页

作者： Rahman, Md. Atiqur Jahan, Israt Islam, Maheen Jabid, Taskeed Ali, Md Sawkat Rashid, Mohammad Rifat Ahmmad Islam, Mohammad Manzurul Ferdaus, Md. Hasanul Rasel, Md Mostofa Kamal Jahan, Mahmuda Rawnak Sharmin, Shayla Rimi, Tanzina Afroz Talukder, Atia Sanjida Matin, Md. Mafiul Hasan Ali, M. Ameer East West Univ Dept Comp Sci & Engn Dhaka 1212 Bangladesh Daffodil Int Univ Dept Comp Sci & Engn Daffodil Smart City 1216 Bangladesh Tesserent Melbourne Vic 3000 Australia

Classifying sleep disorders, such as obstructive sleep apnea and insomnia, is crucial for improving human quality of life due to their significant impact on health. The traditional expert-based classification of sleep stages, particularly through visual inspection, is challenging and prone to errors. This fact highlights the need for accurate machine learning algorithms (MLAs) for analyzing, monitoring, and diagnosing sleep disorders. This paper compares the MLAs for sleep disorder classification, specifically targeting None, Sleep Apnea, and Insomnia, using the Sleep Health and Lifestyle Dataset. We conducted two experiments. In the first one, we selected five key features from the feature spaces using the Gradient Boosting Regressor based on the Mean Decrease Impurity (MDI) technique. We chose two key features using the same methodology in the second experiment. We utilized 15 machine learning classifiers, and Gradient Boosting, Voting, Catboost, and Stacking Classifiers achieved an identical classification accuracy of 97.33%, with Precision, Recall, F1-score of 0.9733, and Specificity of 0.9569 in the original feature space. Among these, Gradient Boosting had the highest AUC of 0.9953 and was 3.36, 5.86, and 20.16 times faster than Voting, Catboost, and Stacking Classifiers, respectively. In the second experiment, the Decision Tree achieved the highest accuracy of 96% in the original and engineered feature spaces and was 149.33 times faster in the engineered feature space. Thus, this research proposes Gradient Boosting as the most effective method, outperforming all state-of-the-art techniques by achieving the highest accuracy, precision, recall, F1-score, and AUC, highlighting its superior classification performance and computational efficiency.

关键词： Sleep Sleep apnea Accuracy Brain modeling machine learning Boosting machine learning algorithms Data models Feature extraction Classification algorithms SMOTEENN ANOVA test feature engineering classification sleep disorder machine learning classifiers ensemble technique

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machine learning-driven classification of Pb-Zn ore deposits using pyrite trace elements and isotopic signatures: A case study of the Gunga deposit

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JOURNAL OF GEOCHEMICAL EXPLORATION 2025年 272卷

作者： Gul, Muhammad Amar Zhang, Huishan Li, Yanguang Yang, Xiaoyong Sun, Chao Zhao, Xiaojian Ren, Guangli Kanwal, Asia Hafeez, Muhammad Yang, Yu Awan, Rizwan Sarwar Faisal, Mohamed Univ Sci & Technol China CAS Key Lab Crust Mantle Mat & Environm Hefei 230026 Peoples R China Univ Sci & Technol China CAS Ctr Excellence Comparat Planetol Hefei 230026 Peoples R China CGS Xian Ctr Geol Survey MNR Key Lab Study Focused Magmatism & Giant Ore D Xian 710054 Peoples R China Kanazawa Univ Dept Math Kanazawa Ishikawa Japan Northeast Petr Univ Daqing Peoples R China Suez Canal Univ Dept Geol Fac Sci El Ismailia 41522 Egypt Cent South Univ Sch Geosci & Info Phys Changsha 410083 Peoples R China

The mineral pyrite, commonly associated with Pb-Zn deposits, can contain a variety of trace elements and is influenced by factors such as temperature, fluid composition, and metal source. These trace elements have long been used to differentiate between various types of Pb-Zn deposits. However, traditional discriminant diagrams, which typically rely on two or three dimensions, fail to comprehensively capture the complex relationships between pyrite trace elements and deposit types. To address this limitation, this study employed four machine learning algorithms-random forest (RF), support vector machine (SVM), gradient boost (GB), and multilayer perceptron (MLP)-to develop classification models based on pyrite trace element compositions. The models were trained on a dataset of 5400 data points from 134 mineral deposits or stratigraphic units with trace element data from published sources. The performance of the classifiers was evaluated via cross-validation using a variant of the leave-one-group-out (LOGO) method. The study applied these machine learning models to newly obtained geochemical data from pyrite samples collected at the Gunga Pb-Zn deposit in the Lasbela-Khuzdar metallogenic belt. The results demonstrated that the classifiers could accurately identify the source of Pb-Zn deposits, producing reliable predictive outcomes. Specifically, the models indicated that the geochemical signature of pyrite from the Gunga deposit was derived from sedimentary-hydrothermal fluids enriched in Pb, Zn, Sb, Tl, As, and Ge, which is consistent with geological and geochemical evidence. The in-situ delta S-34 values of pyrite ranged from -24 parts per thousand to +25 parts per thousand, suggesting that the sulfur in the deposit originated primarily from coeval seawater sulfate. Additionally, Pb isotope compositions indicated crustal sources for Pb-Zn in the Gunga deposit. The combined predictions from the classifiers, along with isotopic analyses of sulfur and lead, suggest t

关键词： Pyrite geochemistry machine learning algorithms Metallogenic discrimination leave-one-group-out cross-validation Gunga deposit CD-type deposits

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HSoMLSDP: A Hybrid Swarm-Optimized machine learning Framework for Software Defect Prediction

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IEEE ACCESS 2025年 13卷 61161-61180页

作者： Das, Madhusmita Mohan, Biju R. Guddeti, Ram Mohana Reddy Natl Inst Technol Karnataka Dept Informat Technol Surathkal 575025 Mangalore India

This research aims to design a hybrid swarm-optimized machine learning software defect prediction (HSoMLSDP) framework to predict software defects. We strive to do this by designing a swarm-optimized machine learning defect prediction (SoMLDP) model within the HSoMLSDP framework. In pursuit of enhancing the defect prediction accuracy of the SoMLDP model, this paper designed two novel hybrid swarm-optimization algorithms (SOAs) referred to as gravitational force grasshopper optimization algorithm-artificial bee colony (GFGOA-ABC), and levy flight grasshopper optimization algorithm-artificial bee colony (LFGOA-ABC) algorithms. By combining the enhanced exploration features of LFGOA and GFGOA with the robust exploitation capacity of the artificial bee colony (ABC), the LFGOA-ABC and GFGOA-ABC algorithms are proposed. Prior to validating the HSoMLSDP framework, the LFGOA-ABC and GFGOA-ABC algorithm's efficacy is first confirmed by experimenting on 19 benchmark functions (BFs) to assess their mean, standard deviation (SD) of optimal values, convergence rate, and convergence rate improvements. Following BFs verification, the second experiment tunes the hyperparameters of the ML models (artificial neural network, XGBOOST) to improve the defect accuracy of the SoMLDP model. The outcomes of the experiments justify a more rapid convergence rate for BFs and notable enhancements of 0.01-0.28 in software defect prediction (SDP) accuracy for NASA defect datasets when compared with state-of-the-art methods. As an enhancement of accuracy justifies the correctness of the SoMLDP model, thus validating the HSoMLSDP framework.

关键词： Optimization Tuning Prediction algorithms Software Convergence Accuracy Software algorithms Predictive models machine learning machine learning algorithms Swarm-optimization algorithm benchmark functions machine learning software defect prediction

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Exploring the process-structure-property relationship of nylon aramid 3D printed composites and parameter optimization using supervised machine learning techniques

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JOURNAL OF ENGINEERED FIBERS AND FABRICS 2025年 20卷

作者： Mohamed, Mohammed Raffic Noor Karuppiah, Ganesh Babu Selvan, Dharani Kumar Saminathan, Rajasekaran Sharma, Shubham Dwivedi, Shashi Prakash Kumar, Sandeep Abbas, Mohamed Kozak, Drazan Lozanovic, Jasmina Nehru Inst Technol Dept Aeronaut Engn Coimbatore 641105 India Chendhuran Coll Engn & Technol Dept Mech Engn Pudukkottai India KPR Inst Engn & Technol Ctr Machining & Mat Testing Dept Mech Engn Coimbatore India Jazan Univ Coll Engn & Comp Sci Dept Mech Engn Jazan Saudi Arabia Lebanese Amer Univ Dept Mech Engn Kraytem 11022801 Beirut Lebanon Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura Punjab India Lloyd Inst Engn & Technol Knowledge Pk 2 Greater Noida Uttar Pradesh India King Khalid Univ Coll Engn Elect Engn Dept Abha City Saudi Arabia Univ Slavonski Brod Mech Engn Fac Slavonski Brod Slavonski Brod Croatia Univ Appl Sci Dept Engn FH Campus WienFavoritenstr 226 A-1100 Vienna Austria

The main goals of this research are to identify the significant input parameters using supervised machine learning methods and investigate the relationship between the process, structure, and properties of components created using fused deposition modeling utilizing nylon aramid composite filaments. To develop an experimental layout using Taguchi's L18 orthogonal array, six different FDM parameters such as infill pattern, infill density, layer thickness, component orientation, print temperature, and raster angle have been taken into *** an Ultimaker FDM printer, rectangular samples were created, and the values of face hardness, thickness regions, printing time, and component weight were assessed. ANOVA and the signal to noise ratio method are two techniques that have been used to find the significant influencing parameter and the ideal combination of parameters. When comparing thickly layered samples with a suitable increase in time and weight, thin-layered samples are shown to have greater hardness values at both tested areas. With a 50.09% contribution to face hardness and a 30.11% contribution to hardness at the thickness area, raster angle is found to be significant over *** thickness is an important element that contributes 81.95% to printing time and 42.09% to part weight, respectively. In an 80:20 train-test split, the decision tree approach outperformed the k-nearest neighbor algorithm for all four output responses, with classification accuracy ranging from 83.33% to 100%. Infill density is recommended by the decision tree method to be extremely significant over face hardness and component weight, and layer thickness is similarly recommended to be highly significant over printing time and hardness at the thickness region. The presence of surface pores, interior voids, and layer abnormalities is confirmed by FESEM images.

关键词： Nylon aramid composite machine learning algorithms fused deposition modeling Decision tree algorithm layer thickness

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