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IEEE International Conference of Online Analysis and Computing Science (ICOACS)

作者： Yuan, Zhengwu Wang, Chaozheng Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing Peoples R China

ISBN: (纸本)9781467377553

In the current age of the Internet, network traffic increased exponentially, either based on user demand for network resources, QoS scheduling, or according to the development trend of network applications for expansion transformation of the existing network, various applications in network traffic need to be classified and identified accurately, network traffic classification is particularly important. C4.5 decision tree algorithm as a commonly used supervised classification algorithm is often applied in traffic classification, but with the increase of data volume, the efficiency of C4.5 algorithm has been reduced. Hadoop platform as open source cloud framework, in dealing with big data has a high performance, so in many cases as the preferred handle large data. On the basis of the original C4.5 algorithm, the improved algorithm is simplified, and the algorithm is parallel to the Hadoop platform, I call it HAC4.5 decision tree algorithm. Experiments show that the improved HAC4.5 decision tree algorithm not only improves the running speed, but also improves the accuracy of the calculation.

关键词： network traffic cloud computing decision tree algorithm classification parallelization

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Improved edge detection algorithm based on decision tree

Improved edge detection algorithm based on decision tree

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International Conference on Mechatronics and Industrial Informatics (ICMII 2013)

作者： Cai Aiping Jiangxi Univ Technol Dept Digital Technol Nanchang 330029 Peoples R China

ISBN: (纸本)9783037856949

The edge detection is the important role in the image disposal. Traditional methods had some limitations more or less in practical applications such as pseudo-edge or need setting parameters by manual. Now, proposed a method can solve these problems in this paper. The histogram of gradient effective features was selected to composite the feature space, and during the process of classifier training, combined with AdaBoost and decision tree algorithm to improve the classification accuracy. Finally, the application of the algorithm proposed to image of Lena edge detection and comparative experimental show that the algorithm has better self-adaptability and good edge can be detected through this new algorithm.

关键词： edge detection AdaBoost algorithm decision tree algorithm

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Machine learning-assisted ratiometric fluorescence sensor array for recognition of multiple quinolones antibiotics

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FOOD CHEMISTRY 2025年 478卷 143722页

作者： Li, Mengyuan Jia, Lei Zhao, Xiaolei Zhang, Lina Zhao, Dan Xu, Jun Zhao, Tongqian Henan Polytech Univ Coll Chem & Chem Engn Jiaozuo 454000 Henan Peoples R China Henan Polytech Univ Inst Resources & Environm Jiaozuo 454000 Henan Peoples R China

Developing analytical methods for simultaneous detection of multiple antibiotic residues is crucial for environmental protection and human health. In this study, a dual lanthanide fluorescence probe (GDP-Eu-Tb) based on nucleotides has been designed. The addition of quinolone antibiotics (QNs) quench the Eu3+ fluorescence signal through the inner filter effect (IFE) and exhibit characteristic peaks, enabling ratio fluorescence detection of levofloxacin (LVLX), gatifloxacin (GTLX), and moxifloxacin (MXLX). A ratiometric fluorescence sensor array is constructed using a single sensor element (GDP-Eu-Tb), combined with principal component analysis (PCA) and decision tree (DT) algorithms to model the relationship between fluorescence intensity ratios (I450/I616, I460/I616, I463/I616, I468/I616) and QNs. The performance of the DT model is evaluated using accuracy, precision, recall, and F1 score, with stability and generalizability confirmed by stratified ten-fold cross-validation. This approach demonstrates high sensitivity, selectivity and applicability and provides an effective solution for antibiotic residue detection.

关键词： Quinolones antibiotics Ratiometric fluorescence sensor array The principal component algorithm decision tree algorithm

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On search of optimal FDM parameter settings for reduced dimensional error using entropy-PCA-TOPSIS and regression machine learning techniques

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART L-JOURNAL OF MATERIALS-DESIGN AND APPLICATIONS 2025年第3期239卷 617-637页

作者： Raffic, N. Mohammed Babu, K. Ganesh Kumar, S. Dharani Parrthipan, B. K. Nehru Inst Technol Dept Aeronaut Engn Coimbatore India Chendhuran Coll Engn & Technol Dept Mech Engn Pudukkottai India KPR Inst Engn & Technol Dept Mech Engn Coimbatore India Kamaraj Coll Engn & Technol Dept Mech Engn Madurai India

Fused deposition modeling (FDM) is a fascinating additive manufacturing technique that has the potential to create prototypes and industrial-grade parts from thermoplastic filaments processed through the extrusion technique. Higher processing time, poor surface finish, and low dimensional accuracy are the major barriers to adopting this technique on a wider scale in industries. The current research has focused on identifying the optimal factor settings for minimizing the dimensional deviations in a simple cuboid model with a circular hole at the center. The study has considered six different FDM factors, such as layer thickness, extrusion temperature, print bed temperature, print speed, raster angle, and part orientation, to understand its impact on errors in dimensional attributes such as length, width, thickness, and hole diameter for nylon filament reinforced with aramid fibers at 8% composition. The measured values are further analyzed for international tolerance (IT) grades, length, and width, resulting in IT08 to IT14, thickness, and hole diameter tolerance grade values ranging from IT09 to IT14. Entropy and principal component analysis methods have been deployed for criteria weight evaluation, and different weight values have been assigned to the experimental outcomes. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) based ranking of alternatives correlates 72.34% (Entropy vs Principal component analysis), with the worst ranked alternative as similar. Regression models developed have R-sq values of 98.15%, 86.27%, 97.63%, and 99.96% for accurately predicting length, width, thickness, and hole diameter changes, respectively. Adaboost algorithm outperformed in training and testing closeness coefficient values with superior performance at all the considered split ratios. The decision tree structure developed indicates layer thickness is significant over the change in length, part orientation has significance over the change in width and

关键词： decision tree algorithm NylonAF80 dimensional accuracy TOPSIS entropy-PCA

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Predicting coastal variations in non-storm conditions with machine learning

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OPEN GEOSCIENCES 2025年第1期17卷

作者： Jabari, Amir Adjami, Mehdi Gharechelou, Saeid Shahrood Univ Technol Fac Civil Engn Dept Water & Environm Engn Coasts Ports & Marine Struct Engn Grp Shahrud Iran Shahrood Univ Technol Fac Civil Engn Dept Geotech & Transport Engn Shahrud Iran

Coastal erosion, driven by natural factors and human activities, is a major threat to vulnerable regions like Narrabeen, Australia. This study investigates shoreline changes, berm crest elevation variations, and horizontal berm crest positions under non-storm conditions. Using a decision tree algorithm, key features influencing these phenomena were identified. For shoreline changes, berm width changes (triangle BW), berm slope, sea level rise (SLR), and wave breaking index (zeta) were critical. Berm crest elevation was linked to BC height, triangle xShoreline, triangle xBC, and wave power (P), while horizontal berm crest positions were influenced by BW, berm slope, triangle yBC, BC height, wave energy (E), SLR, and zeta. The feedforward neural network (FNN) algorithm was then applied to predict these objectives. Shoreline changes were predicted with a root mean squared error (RMSE) of 3.3 m and R 2 of 92% (DS4 scenario). Berm crest elevation predictions achieved an RMSE of 0.35 m and R 2 of 75% (DY4 scenario), while horizontal berm crest positions reached an RMSE of 9.28 m and R 2 of 85.8% (DX7 scenario). These results demonstrate that parameter classification via decision trees enhances neural network predictions. The FNN proved to be a reliable tool for forecasting coastal dynamics, supporting effective monitoring and management strategies.

关键词： coastal berm coastal morphodynamic non-storm condition decision tree algorithm artificial neural network

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Analyzing factors influencing the severity of occupational accidents in textile industry using decision tree algorithms

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第1期27卷 787-825页

作者： Mutlu, Nazli Gulum Altuntas, Serkan Bingol Univ Dept Occupat Hlth & Safety TR-12000 Bingol Turkiye Yildiz Tech Univ Dept Ind Engn TR-34349 Istanbul Turkiye

Analysis of factors influencing the severity of occupational accidents in manufacturing systems is of vital importance for sustainable manufacturing in a business environment. The aim of this study is to identify occupational health and safety risks in the Turkish textile manufacturing industry, which includes "manufacturing of textiles," "manufacturing of wearing apparel," and "manufacturing of leather and related products". There is no study in the literature that examines the risks related to occupational health and safety in Turkish textile manufacturing based on occupational accident records from 2013 to 2019. To fill this gap in the literature, data-driven modeling is conducted to analyze 139,092 accident records including enterprise information, accident information, injured person information, and accident outcome information, which are obtained from the Ministry of Family, Labor, and Social Services in Turkiye. The result of this study showed that there are 50 injury accident decision rules based on 15 distinct accident predictors, which will support decisions for the efficient use of limited resources, the development of effective accident prevention policies, and the stability of the sector.

关键词： Turkish textile industry Risk management Occupational accidents decision tree algorithm

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A Comparative Assessment of decision tree algorithms for Index of Sediment Connectivity Modelling

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WATER RESOURCES MANAGEMENT 2024年第7期38卷 2293-2313页

作者： Asadi, Haniyeh Dastorani, Mohammad T. Sidle, Roy C. Jahanshahi, Afshin Ferdowsi Univ Mashhad Fac Nat Resources & Environm Mashhad Iran Univ Cent Asia Mt Soc Res Inst Bishkek Kyrgyzstan Sari Agr Sci & Nat Resources Univ Dept Watershed Management Sari Iran

Assessment of the spatial distribution of potential pathways of sediment transport and the degree of linkage between sediment sources and the channel network within a watershed represents a valuable analysis for informing management decisions on sediment yield and transfer. Given the limitations of conventional methods for determining index of sediment connectivity (IC), there is a need to provide a flexible and efficient approach with the ability to apply different factors. In this regard, five decision tree-based machine learning models: M5 prime (M5P), random tree (RT), random forest (RF), alternating model tree (AMT), and reduced error pruning tree (REPT) were tested using geomorphic and climatic factors. Two databases were constructed with 200 and 1600 classes at 50 watersheds in Queensland, Australia. In these models, IC was assessed as an output parameter and six attributes that affect IC were assigned as input parameters (i.e., elevation, slope, area, length of stream channel, normalized difference vegetation index, and rainfall). Statistical validation and comparison of model predictions with calculated IC values based on the approach of Borselli et al. (Catena 75:268-277, 2008) were performed. Based on the statistical criteria, the RF model produced the most robust estimations of IC compared to other models and performed very well for IC modelling, especially in smaller subsections of watersheds. Accordingly, these findings can play an effective role for implementing watershed management and soil and water resources management measures.

关键词： Sediment connectivity Machine learning decision tree algorithm Random forest Geomorphic factors

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Predicton of MRR & Surface Roughness in Wire EDM Machining using decision tree and Naive Bayes algorithm

Predicton of MRR & Surface Roughness in Wire EDM Machining u...

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3rd IEEE International Conference on Emerging Smart Computing and Informatics (IEEE-ESCI)

作者： Yogesh, L. Arunadevi, M. Prakash, C. P. S. Dayananda Sagar Coll Engn Dept Mech Engn Bangalore Karnataka India

ISBN: (纸本)9781728185194

Manufacturing industries facing problem in optimal selection of process parameters in machining process. Finding optimum process parameters for achieving maximum Material Removal Rate and minimum Surface Roughness is a challenging task and it requires lot of time and energy for experimentation trails or experience. It wastes lot of resources and money, sometimes ends up with negative results. To overcome the above issue, this paper presents an algorithm for prediction of Surface Roughness and Material Removal rate using decision tree algorithm and Naive Bayes algorithm without experimentation. Lot of resources and time can be saved using these machine learning algorithms. In this paper, Material removal rate and Surface roughness of EDM machining of Aluminum composites is predicted using decision tree algorithm and Naive Bayes algorithm. Then the model can be used to predict the Material Removal Rate and Surface finish of any combination process parameters before machining process.

关键词： decision tree algorithm EDM Optimization Naive Bayes algorithm Surface Roughness Machine Learning

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Analysis of Rigid Pavement Distresses on Interstate Highway Using decision tree algorithms

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KSCE JOURNAL OF CIVIL ENGINEERING 2010年第2期14卷 123-130页

作者： Kang, Myungook Kim, Minkwan Lee, Joo Hyoung Purdue Univ Calumet Dept Construct Sci & Org Leadership Hammond IN 46323 USA MMI Engn Houston TX 77077 USA Univ Wisconsin Dept Civil & Environm Engn Madison WI 53706 USA

This paper describes the application of two decision tree algorithms, Logistic Regression trees with Unbiased Selection (LOTUS) and Classification Rule with Unbiased Interaction Selection and Estimation (CRUISE), to identify prevailing distress types that affect pavement maintenance decision making. The maintenance history of the selected sections within Interstate highway I-90 in the State of Wisconsin is modeled to build decision trees. The historical field distress survey data include the types, quantities, severities, and locations of distresses. The primary objective of this paper is to present the decision tree algorithms that may be incorporated into the decision making process and help state highway agencies make rational decisions during the course of pavement management. The paper discusses the model development process and prevailing distress identification procedures employing the decision tree algorithms. A case study is conducted on another Interstate highway, I-43, with a view to furthering the analysis of the results from the two algorithms. These analysis models and the results will provide the state highway agencies and practitioners with a means to identify prevailing distress types in a specific location and facilitate making decisions on future maintenance practices.

关键词： Pavement Management System (PMS) maintenance decision support highway maintenance factors field distress survey decision tree algorithm

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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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