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Business Optimization of Financial Centers in Pharmaceutical Enterprises Based on Robotic Process Automation Technology

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IEEE ACCESS 2025年 13卷 51012-51026页

作者： Wang, Yali Zhou, Weiwei Li, Yingji Sun, Jingqi SIAS Univ Sch Business Zhengzhou 451150 Peoples R China Stamford Int Univ Grad Sch Bangkok 10250 Thailand Yunnan Univ Chinese Med Sch Humanities & Management Kunming 650500 Peoples R China Univ Philippines Coll Educ Arts & Sci Manila 1008 Philippines

With the continuous advancement of technology and the rapid development of automation technology, pharmaceutical companies urgently need to optimize and upgrade their financial operations with higher efficiency. To optimize the business services of the financial center of pharmaceutical enterprises, the machine learning algorithm and robotic process automation technology are used to design suitable optimization methods. Based on the analysis of the departmental structure and business generation of the financial center of pharmaceutical enterprises, the optimization focus of business requirements is determined. Next, the decision tree algorithm is introduced to visually demonstrate the classification and decision-making process, optimizing the classification operation. Combined with robotic process automation technology, highly repetitive tasks are simplified to optimize business. The test results showed that the decision tree algorithm accurately classified different types of samples into the correct category when distinguishing them. The recall rate on reasonable financing schemes reached 0.69. The business processing time of robotic process automation technology was reduced by 50%, and the task completion rate was increased by 27%. The results indicate that the research designed business optimization method for pharmaceutical enterprise financial centers based on robot process automation technology significantly improves business processing efficiency, effectively controls costs, and enhances operational flexibility. In addition, the research provides theoretical support and practical guidance for the construction of intelligent financial center systems, promotes the automation and intelligent transformation of financial management, and makes important contributions to the development of this field.

关键词： Business Companies Finance Drugs Costs Intelligent automation decision trees Accuracy Research and development Biological system modeling Robotic process automation pharmaceutical companies financial center decision tree algorithm business optimization

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An Improved Medical decision Model Based on decision tree algorithms 6

An Improved Medical Decision Model Based on Decision Tree Al...

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2016 IEEE International Conferences on BDCloud/SocialCom/SustainCom 2016

作者： Duan, Guiduo Ding, Dan Tian, Yuan You, Xiaoming Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Univ Elect Sci & Technol China Sch Phys Elect Chengdu Peoples R China 32 Inst China Elect Technol Grp Corp Shanghai Peoples R China Tongji Univ Sch Software Engn Shanghai Peoples R China

ISBN: (纸本)9781509039364

In the medical decision field, the conventional analysis methods of medical insurance data are lack of flexibility and efficiency. When coping with huge and redundant medical dataset, it is difficult to extract the candidate attributes if only using some limited professional knowledge. Therefore, the correlations between the medical insurance cost and relevant factors (e.g. diagnosis results) need to be mined by techniques such as the FP-Growth algorithm, which uses an extended prefix-tree structure for building attributes automatically, storing compressed and crucial information. In this work, we propose an improved decision tree algorithm to effectively model medical data, which includes processes of pre-pruning and post-pruning. Classification based on three criteria such as accuracy, stability and complexity are then utilized to improve the effectiveness of our approach. Simulation results show that our proposed model can improve the decision making with higher flexibility and efficiency.

关键词： decision tree algorithm pruning method FP Growth algorithm medical decision model

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Optimizing Rotation Forest-Based decision tree algorithms for Groundwater Potential Mapping

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WATER 2023年第12期15卷 2287页

作者： Chen, Wei Wang, Zhao Wang, Guirong Ning, Zixin Lian, Boxiang Li, Shangjie Tsangaratos, Paraskevas Ilia, Ioanna Xue, Weifeng Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Peoples R China No 7 Oil Prod Plant Changqing Oilfield Branch Petr Qingyang 745700 Peoples R China Shenmu Ningtiaota Coal Min Co Ltd Shaanxi Coal & Chem Ind Grp Co Ltd Yulin 719300 Peoples R China Natl Tech Univ Athens Sch Min & Met Engn Dept Geol Sci Lab Engn Geol & Hydrogeol Zografos 15780 Greece Shaanxi Coal & Chem Technol Inst Co Ltd Xian 710065 Peoples R China

Groundwater potential mapping is an important prerequisite for evaluating the exploitation, utilization, and recharge of groundwater. The study uses BFT (best-first decision tree classifier), CART (classification and regression tree), FT (functional trees), EBF (evidential belief function) benchmark models, and RF-BFtree, RF-CART, and RF-FT ensemble models to map the groundwater potential of Wuqi County, China. Firstly, select sixteen groundwater spring-related variables, such as altitude, plan curvature, profile curvature, curvature, slope angle, slope aspect, stream power index, topographic wetness index, stream sediment transport index, normalized difference vegetation index, land use, soil, lithology, distance to roads, distance to rivers, and rainfall, and make a correlation analysis of these sixteen groundwater spring-related variables. Secondly, optimize the parameters of the seven models and select the optimal parameters for groundwater modeling in Wuqi County. The predictive performance of each model was evaluated by estimating the area under the receiver operating characteristic (ROC) curve (AUC) and statistical index (accuracy, sensitivity, and specificity). The results show that the seven models have good predictive capabilities, and the ensemble model has a larger AUC value. Among them, the RF-BFT model has the highest success rate (AUC = 0.911), followed by RF-FT (0.898), RF-CART (0.894), FT (0.852), EBF (0.824), CART (0.801), and BFtree (0.784), respectively. Groundwater potential maps of these 7 models were obtained, and four different classification methods (geometric interval, natural breaks, quantile, and equal interval) were used to reclassify the obtained GPM into 5 categories: very low (VLC), low (LC), moderate (MC), high (HC), and very high (VHC). The results show that the natural breaks method has the best classification performance, and the RF-BFT model is the most reliable. The study highlights that the proposed ensemble model has more effi

关键词： groundwater potential mapping rotation forest decision tree algorithm ROC China

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An improved Network Traffic Classification algorithm Based on Hadoop decision tree

An improved Network Traffic Classification Algorithm Based o...

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2016 IEEE International Conference of Online Analysis and Computing Science(ICOACS 2016)

作者： Zhengwu Yuan Chaozheng Wang College of Computer Science and Technology Chongqing University of Posts and Telecommunications

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.C4.5decision 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 C4.5algorithm has been *** platform as open source cloud framework,in dealing with big data has a high performance,so in many cases as the preferred handle large *** 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 *** 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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Modelling of shaft unbalance: Modelling a multi discs rotor using K-Nearest Neighbor and decision tree algorithms

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MEASUREMENT 2020年 151卷 107253-000页

作者： Gohari, Mohammad Eydi, Amir Mohammad Arak Univ Technol Mech Engn Arak Iran

Multi discs rotors are widely used in the industry. Shaft unbalance in multi-discs' rotors is the main failure origin that leads to global failures in rotary systems. Unbalance parameters that must be detected in the shaft are focused on this study. Unbalance parameters are eccentric mass value, eccentric radius, and disc number which are presenting an unbalance location. The main aim of the current paper is to identify unbalance parameters of a rotating shaft having multi-discs by artificial intelligent methods namely KNN and decision tree algorithm. For both algorithms, data derived from a fabricated test rig consists of a shaft in which four discs are mounted on that. The results show that the KNN presents more accuracy in estimating of unbalance parameters compared to the decision tree in terms of unbalance locating. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Unbalance Multi-discs rotor K-Nearest Neighbor (KNN) algorithm decision tree algorithm

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Classification of start-ups' digital marketing adoption experiences: an investigation of characteristics and interactions

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FUTURE BUSINESS JOURNAL 2025年第1期11卷 1-10页

作者： El-Shihy, Dina Hassan, Noha NewGiza Univ NGU Sch Business & Finance km 22 Cairo Alex Desert Rd Cairo Egypt

PurposeThis study aims to classify various start-ups based on their experiences with digital marketing adoption. It examines the characteristics of these start-ups and how they interact to influence the adoption ***/methodology/approachA quantitative research approach was adopted, utilizing questionnaires administered to 205 founders and managers of Egyptian start-ups. Data were collected on the usage of digital marketing tools, level of utilization, duration of adoption, resource allocation, and budget. A nonparametric decision tree algorithm, visualized using R, was employed for data *** study identifies key characteristics of start-ups that influence their classification in terms of digital marketing adoption. Factors such as digital marketing experience, time and budget allocation, and utilization levels significantly impact this classification. Furthermore, website usage emerged as a crucial criterion distinguishing basic from moderate digital marketing adopters among Egyptian *** research enhances the understanding of digital marketing adoption among Egyptian start-ups. By classifying start-ups based on demographics and digital marketing practices using a decision tree algorithm, it offers a novel perspective on the topic.

关键词： Start-up characteristics Digital marketing adoption Digital marketing tools decision tree algorithm R visualization Entrepreneurial ecosystem Cost-effectiveness Marketing strategy Adoption years Budget allocation

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Analysing Effectiveness of Sentiments in Social Media Data Using Machine Learning Techniques

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Journal of Computer and Communications 2025年第1期13卷 136-151页

作者： Thambusamy Velmurugan Mohandas Archana Ajith Singh Nongmaithem PG & Research Department of Computer Science D.G.Vaishnav College Chennai India PG Department of IT & BCA D.G.Vaishnav College Chennai India Department of Computer Science Souht East Manipur College Manipur India

Every second, a large volume of useful data is created in social media about the various kind of online purchases and in another forms of reviews. Particularly, purchased products review data is enormously growing in different database repositories every day. Most of the review data are useful to new customers for theier further purchases as well as existing companies to view customers feedback about various products. Data Mining and Machine Leaning techniques are familiar to analyse such kind of data to visualise and know the potential use of the purchased items through online. The customers are making quality of products through their sentiments about the purchased items from different online companies. In this research work, it is analysed sentiments of Headphone review data, which is collected from online repositories. For the analysis of Headphone review data, some of the Machine Learning techniques like Support Vector Machines, Naive Bayes, decision trees and Random Forest algorithms and a Hybrid method are applied to find the quality via the customers’ sentiments. The accuracy and performance of the taken algorithms are also analysed based on the three types of sentiments such as positive, negative and neutral.

关键词： Support Vector Machine Random Forest algorithm Naive Bayes algorithm Machine Learning Techniques decision tree algorithm

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The rainfall-induced landsliding in Western Serbia: A temporal prediction approach using decision tree technique

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ENGINEERING GEOLOGY 2018年 232卷 147-159页

作者： Marjanovic, Milos Krautblatter, Michael Abolmasov, Biljana Duric, Uros Sandic, Cvjetko Nikolic, Velizar Tech Univ Munich Chair Landslide Res Fac Civil Geo & Environm Engn Arcisstr 21 Munich Germany Univ Belgrade Fac Min & Geol Dusina 7 Belgrade 11000 Serbia Univ Belgrade Fac Civil Engn Bulevar Kralja Aleksandra 73 Belgrade Serbia Geol Survey Republ Srpska Vuka Karadz 148B Zvornik Bosnia & Herceg Minist Min & Energy Republ Serbia Omladinskih Brigada 1 Belgrade Serbia

This paper focuses on modeling rainfall-induced massive landsliding in the Western Serbia in the 2001-2014 period. The motivation for conducting the study was the rainfall-induced flooding and landsliding that took place across most of the Serbia and Bosnia and Herzegovina in May 2014, and had devastating effects, including human casualties, and destruction of natural and urban environment. In the first part of the study, the general analysis was conducted. It includes a wide area (70,000 km(2)), wherein spatial rainfall patterns were identified using the monthly rainfall data from the 2001-2014. Areas that have higher monthly precipitation than the baseline monthly rainfall (1961-90) were outlined. One location within these zones was chosen as critical Loznica in Western Serbia. The area of Loznica was further examined: comparison between local daily rainfall and local landslide events recorded in 2001-2014;correlation between specific rainfall conditions, i.e. cumulative rainfall for different time windows, and the landsliding events in the specified period;identification of additional non-reported rainfall events that were potentially responsible for landsliding;analyses of the rainfall thresholds and temporal rainfall distribution. The decision tree algorithm was used to identify rainfall conditions that triggered landslides in the specified period. It was hypothesized that short-term rainfall has less influence on massive landsliding than the mid/long-term rainfall. Unlike other black-box techniques, decision tree-based modeling gives a good insight into the thresholding process. Namely, it was possible to follow the decision tree structure and reconstruct the critical cumulative rainfall distribution and thresholds that have led to landsliding. The main findings suggest that a high-yield mid-term rainfall (2 and 3-day rainfall) are the most important for massive landsliding, while long-term cumulative rainfall (30-day) has some additional influence in the case

关键词： Landslides Rainfall threshold decision tree algorithm Serbia Bosnia and Herzegovina

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Artificial neural network and decision tree facilitated prediction and validation of cytokinin-auxin induced in vitro organogenesis of sorghum (Sorghum bicolor L.)

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PLANT CELL TISSUE AND ORGAN CULTURE 2023年第3期153卷 611-624页

作者： Aasim, Muhammad Ali, Seyid Amjad Altaf, Muhammad Tanveer Ali, Amjad Nadeem, Muhammad Azhar Baloch, Faheem Shahzad Sivas Univ Sci & Technol Fac Agr Sci & Technol Sivas Turkiye Bilkent Univ Dept Informat Syst & Technol Ankara Turkiye

In this study, in vitro regeneration protocol of sorghum (Sorghum bicolor) was successfully established by using direct organogenesis from a mature zygotic embryo explant. The used basal medium encompassed Murashige and Skoog medium (MS) supplemented with 2-4 mg/L Benzylaminopurine (BAP) alone or with 0.25 mg/L Indole butyric acid (IBA) or Naphthalene acetic acid (NAA). Results demonstrated a significant impact of cytokinin-auxin on shoot count (1.24-3.46) and shoot length (2.80-3.47 cm). Maximum shoot count (3.46) and shoot length (3.97 cm) were achieved on the MS medium enriched with 2 mg/L BAP + 0.25 mg/L NAA and 2.0 mg/L BAP, respectively. To ascertain the impact of BAP alone, BAP + IBA, and BAP + NAA, the data were also analyzed by using a factorial regression model. Pareto chart and normal plots were used to check either the positive or negative impact of input variables on output variables. To further explore the association between BAP + IBA and BAP + NAA on shoot count and shoot length, contour and surface plots were also built. Three different artificial intelligence-based models along with four different performance metrics were utilized to validate the predicted results. Multilayer perceptron (MLP) model performed more efficiently (R-2 = 0.799 for shoot count and R-2 = 0.831 for shoot length) as compared to the decision tree-based algorithms of random forest (RF) - (R-2 = 0.779 for shoot count and R-2 = 0.786 for shoot length) and extreme gradient boost (XGBoost) - (R-2 = 0.768 for shoot count and R-2 = 0.781 for shoot length). As plant tissue culture protocol is a powerful tool for genetic engineering and genome editing of crops, integration of different artificial intelligence-based models can lead to improvement of sorghum with the aid of biotechnological tools. [GRAPHICS]

关键词： Artificial intelligence Auxins Cytokinin In vitro Multilayer perceptron decision tree algorithm Regeneration

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A traffic accident morphology diagnostic model based on a rough set decision tree

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TRANSPORTATION PLANNING AND TECHNOLOGY 2016年第8期39卷 751-758页

作者： Tao, Gang Song, Huansheng Liu, Jun Zou, Jiao Chen, Yanxiang Changan Univ Sch Informat Engn Xian Peoples R China Anhui Keli Informat Ind Co Ltd Hefei Peoples R China Minist Publ Secur Key Lab Urban ITS Technol Optimizat & Integrat Hefei Peoples R China Hefei Univ Technol Sch Comp & Informat Hefei Peoples R China

To build a traffic safety feature model and to quantify accident influences caused by some traffic violation behaviors of drivers, an accident diagnostic decision-making model is established. For the purpose of diagnosing accident morphologies, rough set theory is applied and the influence of traffic factors of different accident morphologies is quantified through calculating the degree of attribute importance, selecting core traffic factors and adopting a C4.5 decision tree algorithm. In the paper, road traffic accident data from 2008 to 2013 in Anhui Province are used. Typical rules are selected, targeted strategy proposals are put forward, and then, a scientific and reasonable diagnostic basis is provided for the diagnosis of traffic safety risks and the prediction of potential traffic accidents.

关键词： Traffic accidents rough set decision tree algorithm C4 5 accident morphology decision diagnosis

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