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Journal articles on the topic 'Decision Based Algorithm'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Singh, Surya Partap, Amitesh Srivastava, Suryansh Dwivedi, and Mr Anil Kumar Pandey. "AI Based Recruitment Tool." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2815–19. http://dx.doi.org/10.22214/ijraset.2023.52193.

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Abstract: In this study, the researchers narrowed their focus to the application of algorithmic decision-making in ranking job applicants. Instead of comparing algorithms to human decision-makers, the study examined participants' perceptions of different types of algorithms. The researchers varied the complexity and transparency of the algorithm to understand how these factors influenced participants' perceptions. The study explored participants' trust in the algorithm's decision-making abilities, fairness of the decisions, and emotional responses to the situation. Unlike previous work, the st

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Feng, Guoxu, Songbo Gu, and Shihu Sun. "Intelligent Ship Collision Avoidance Support System Based on the Algorithm of Anthropomorphic Physics." International Journal of Ambient Computing and Intelligence 15, no. 1 (2024): 1–20. https://doi.org/10.4018/ijaci.365340.

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Most of the collision-related decisions of ships at sea depend on the working experience of drivers and determining a reasonable avoidance decision quickly when facing a multivessel encounter situation is difficult, so applying intelligent algorithms to assist these decisions is necessary. On the basis of this, the authors researched the construction of intelligent decision support systems for ship collision avoidance that relies on an anthropomorphic physics optimization algorithm. They used this algorithm to obtain the global range optimal solutions through iteration, which provides effectiv

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Ma, Xiuqin, Yanan Wang, Hongwu Qin, and Jin Wang. "A Decision-Making Algorithm Based on the Average Table and Antitheses Table for Interval-Valued Fuzzy Soft Set." Symmetry 12, no. 7 (2020): 1131. http://dx.doi.org/10.3390/sym12071131.

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Interval-valued fuzzy soft set is one efficient mathematical model employed to handle the uncertainty of data. At present, there exist two interval-valued fuzzy soft set-based decision-making algorithms. However, the two existing algorithms are not applicable in some cases. Therefore, for the purpose of working out this problem, we propose a new decision-making algorithm, based on the average table and the antitheses table, for this mathematical model. Here, the antitheses table has symmetry between the objects. At the same time, an example is designed to prove the availability of our algorith

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VUKIĆEVIĆ, MILAN, MILOŠ JOVANOVIĆ, BORIS DELIBAŠIĆ, SONJA IŠLJAMOVIĆ, and MILIJA SUKNOVIĆ. "REUSABLE COMPONENT-BASED ARCHITECTURE FOR DECISION TREE ALGORITHM DESIGN." International Journal on Artificial Intelligence Tools 21, no. 05 (2012): 1250022. http://dx.doi.org/10.1142/s0218213012500224.

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Many decision tree algorithms were proposed over the last few decades. A lack of publishing standards for decision tree algorithm software produced a large time gap between algorithm proposals and their wider application in practice. Non-existence of common repository for storing algorithms and their parts led to a need to re-implement these algorithms from a scratch when they had to be implemented on a different platform. This makes the comparison between algorithms and their partial improvements vague. In addition, combinations and interactions between different algorithm parts haven't been

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Shakya, Subarna. "Probabilistic Neural Network based Managing Algorithm for Building Automation System." December 2021 3, no. 4 (2021): 272–83. http://dx.doi.org/10.36548/jaicn.2021.4.001.

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A building automation system is a centralized intelligent system, which controls the operation of energy, security, water, and safety by the help of hardware and software modules. The general software modules employed for automation process have an algorithm with pre-determined decisions. However, such pre-determined decision algorithms won’t work in a proper manner at all situations like a human brain. Therefore a human biological inspired algorithms are developed in recent days and termed as neural network algorithms. The Probabilistic Neural Network (PNN) is a kind of artificial neural netw

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EL-GHAMRAWY, SALLY M., and ALI I. ELDESOUKY. "AN AGENT DECISION SUPPORT MODULE BASED ON GRANULAR ROUGH MODEL." International Journal of Information Technology & Decision Making 11, no. 04 (2012): 793–820. http://dx.doi.org/10.1142/s0219622012500216.

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A multi-agent system (MAS) is a branch of distributed artificial intelligence, composed of a number of distributed and autonomous agents. In a MAS, effective coordination is essential for autonomous agents to achieve their goals. Any decision based on a foundation of knowledge and reasoning can lead agents into successful cooperation; to achieve the necessary degree of flexibility in coordination, an agent must decide when to coordinate and which coordination mechanism to use. The performance of any MAS depends directly on the decisions made by the agents. The agents must therefore be able to

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Pan, Yunfei, Hongyong Jia, Wenhe Liu, He Sun, Mingyang Xu, and Cen Chen. "Mimicry API Gateway Decision Algorithm Based on Trust Distribution." Journal of Physics: Conference Series 2424, no. 1 (2023): 012004. http://dx.doi.org/10.1088/1742-6596/2424/1/012004.

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Abstract Aiming at the lack of dynamic and comprehensiveness of trust evaluation methods in current adjudication algorithms, an adjudication algorithm for mimic API gateway based on trust distribution is proposed. The algorithm comprehensively considers the direct and indirect trust relationships between equipment executives from the perspective of trust distribution, and on this basis introduces a dynamic penalty strategy, which greatly improves the dynamics and effectiveness of the algorithm. In addition, the algorithm is simulated and tested in the simulated API gateway environment, and the

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Guo, Jinlin, Haoran Wang, Xinwei Li, and Li Zhang. "Stream Classification Algorithm Based on Decision Tree." Mobile Information Systems 2021 (December 21, 2021): 1–11. http://dx.doi.org/10.1155/2021/3103053.

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Due to the rise of many fields such as e-commerce platforms, a large number of stream data has emerged. The incomplete labeling problem and concept drift problem of these data pose a huge challenge to the existing stream data classification methods. In this respect, a dynamic stream data classification algorithm is proposed for the stream data. For the incomplete labeling problem, this method introduces randomization and iterative strategy based on the very fast decision tree VFDT algorithm to design an iterative integration algorithm, and the algorithm uses the previous model classification r

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Kašćelan, Ljiljana, and Vladimir Kašćelan. "Component-Based Decision Trees." International Journal of Operations Research and Information Systems 6, no. 4 (2015): 1–18. http://dx.doi.org/10.4018/ijoris.2015100101.

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Popular decision tree (DT) algorithms such as ID3, C4.5, CART, CHAID and QUEST may have different results using same data set. They consist of components which have similar functionalities. These components implemented on different ways and they have different performance. The best way to get an optimal DT for a data set is one that use component-based design, which enables user to intelligently select in advance implemented components well suited to specific data set. In this article the authors proposed component-based design of the optimal DT for classification of securities account holders

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Sirajuddin Hawari, Muhammad Zulfikri Maulanab, Teuku Binzar Nawaf Musyaffa, Desy Komalasari, and Mutiara Persada Pulungan. "MINIMAX ALGORITHM BASED ON "MAIN INI YUK" GAME." Jurnal ilmiah Sistem Informasi dan Ilmu Komputer 2, no. 1 (2022): 12–17. http://dx.doi.org/10.55606/juisik.v2i1.318.

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In the game there are rules for each game. The rules are made as a challenge to achieve victory. To achieve victory requires analysis of the right algorithm in making decisions. Minimax is one of the best decision-making algorithms to be applied in a game. The time complexity of the minimax algorithm is O(b^m) which b is the branching and m is the depth. 

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Qin, Hongwu, Yanan Wang, Xiuqin Ma, and Jin Wang. "A Novel Approach to Decision Making Based on Interval-Valued Fuzzy Soft Set." Symmetry 13, no. 12 (2021): 2274. http://dx.doi.org/10.3390/sym13122274.

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Interval-valued fuzzy soft set theory is a powerful tool that can provide the uncertain data processing capacity in an imprecise environment. The two existing methods for decision making based on this model were proposed. However, when there are some extreme values or outliers on the datasets based on interval-valued fuzzy soft set for making decisions, the existing methods are not reasonable and efficient, which may ignore some excellent candidates. In order to solve this problem, we give a novel approach to decision making based on interval-valued fuzzy soft set by means of the contrast tabl

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Lei, Kai, Xiaoning Zhu, Jianfei Hou, and Wencheng Huang. "Decision of Multimodal Transportation Scheme Based on Swarm Intelligence." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/932832.

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In this paper, some basic concepts of multimodal transportation and swarm intelligence were described and reviewed and analyzed related literatures of multimodal transportation scheme decision and swarm intelligence methods application areas. Then, this paper established a multimodal transportation scheme decision optimization mathematical model based on transportation costs, transportation time, and transportation risks, explained relevant parameters and the constraints of the model in detail, and used the weight coefficient to transform the multiobjective optimization problems into a single

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Kohut, Yurii, and Iryna Yurchak. "Recommendation System for Purchasing Goods Based on the Decision Tree Algorithm." Advances in Cyber-Physical Systems 6, no. 2 (2021): 121–27. http://dx.doi.org/10.23939/acps2021.02.121.

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Over the past few years, interest in applications related to recommendation systems has increased significantly. Many modern services create recommendation systems that, based on user profile information and his behavior. This services determine which objects or products may be interesting to users. Recommendation systems are a modern tool for understanding customer needs. The main methods of constructing recommendation systems are the content-based filtering method and the collaborative filtering method. This article presents the implementation of these methods based on decision trees. The co

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Zong, Licheng, and Nana Wang. "A Product Modeling Design Decision Model Based on PGA Genetic Algorithm." Mathematical Problems in Engineering 2022 (August 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/7794320.

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A Pareto-based genetic algorithm (PGA) product design decision model is proposed in this work to improve the efficiency of product design decisions and avoid the instability of individual decision differences. Based on the product modeling design decision constraint space, decision variables, and other factors, the model utilizes the PGA optimization algorithm to make an objective decision on a design scheme. Using the analytic hierarchy process, the design expectations, objectives, variables, and schemes are constructed into a hierarchical structure. The design decision problems are then mapp

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Liu, Yu. "Discussion on the Enterprise Financial Risk Management Framework Based on AI Fintech." Decision Making: Applications in Management and Engineering 7, no. 1 (2023): 254–69. http://dx.doi.org/10.31181/dmame712024942.

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Deep learning algorithms lack interpretability and interpretability in the decision-making process. This makes it difficult to understand the judgment basis and decision-making process of financial risks based on algorithms, which may reduce the trust and acceptance of risk decisions by enterprises. To address this issue, this study introduces the improved random forest algorithm based on the decision tree algorithm to discuss its framework. Through analysis of the PR curve in the experiment, it was determined that the AP value of the enhanced random forest algorithm is 0.9919, a significant i

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Wang, Shiqian, Wuqi Gao, and Songhan Wang. "Research on Machine Learning Program Generation Algorithm Based on AORBCO." International Journal of Advanced Network, Monitoring and Controls 9, no. 2 (2024): 23–36. http://dx.doi.org/10.2478/ijanmc-2024-0013.

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Abstract The design and development of machine learning programs require selecting appropriate data and algorithms, and coding and debugging based on specific task requirements and the programming experience of developers. However, the current knowledge structure in the field of machine learning is relatively complex, lacking systematic organization, and developers often face the problem of lack of experience when choosing algorithms and designing programs, resulting in a long development cycle and easy errors in machine learning programs. In response to the above issues, this article proposes

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Jin, Maozhu, Hua Wang, Qian Zhang, and Cheng Luo. "Financial Management and Decision Based on Decision Tree Algorithm." Wireless Personal Communications 102, no. 4 (2018): 2869–84. http://dx.doi.org/10.1007/s11277-018-5312-6.

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STEBELETSKYI, Myroslav, Eduard MANZIUK, Tetyana SKRYPNYK, and Ruslan BAHRIY. "METHOD OF BUILDING ENSEMBLES OF MODELS FOR DATA CLASSIFICATION BASED ON DECISION CORRELATIONS." Herald of Khmelnytskyi National University. Technical sciences 315, no. 6(1) (2022): 224–33. http://dx.doi.org/10.31891/2307-5732-2022-315-6-224-233.

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The scientific work highlights the problem of increasing the accuracy of binary classification predictions using machine learning algorithms. Over the past few decades, systems that consist of many machine learning algorithms, also called ensemble models, have received increasing attention in the computational intelligence and machine learning community. This attention is well deserved, as ensemble systems have proven to be very effective and extremely versatile in a wide range of problem domains and real-world applications. One algorithm may not make a perfect prediction for a particular data

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Parfenov, V. I., and V. D. Le. "DISTRIBUTED DETECTION BASED ON USING SOFT DECISION DECODING IN A FUSION CENTER." Telecommunications, no. 1 (2022): 2–9. http://dx.doi.org/10.31044/1684-2588-2022-0-1-2-9.

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In this work, distributed detection by data from many sensors in the wireless sensor system is considered. A decision-making algorithm based on using soft-decision decoding in a fusion center is synthesized. Its gain in efficiency compared to efficiency of the algorithm based on hard-decision scheme is shown. It is noted that this algorithm is a generalization of earlier developed algorithms.

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ZHAO, S. J. "CONSTRUCTION OF ENTERPRISE ECONOMIC DECISION RECOMMENDATION SYSTEM BASED ON COMBINED ASSOCIATION ANALYSIS MODEL." Latin American Applied Research - An international journal 48, no. 4 (2018): 249–54. http://dx.doi.org/10.52292/j.laar.2018.236.

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With the development of the information age and the network economy, enterprises need more and more decisions, and the difficulty and complexity of decision-making are constantly improving. The traditional centralized decision-making is no longer in line with the requirements of current social and economic development. Therefore, based on the in-depth study of conventional algorithms, this paper constructs a portfolio association analysis model for enterprise economic decision-making recommendation system, and uses the off-line test method to test the construction model. The test results show

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XUE, B. X., and T. LIU. "INVESTMENT DECISION OF TOURISM LEISURE PROJECT BASED ON COLLABORATIVE FILTERING ALGORITHM." Latin American Applied Research - An international journal 48, no. 4 (2018): 293–97. http://dx.doi.org/10.52292/j.laar.2018.243.

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In the era of the Big Bang, users have to spend a lot of time looking for the information they really need, and the search engine can't present information that is not described by the user. Based on the User based collaborative filtering recommendation algorithm and the collaborative filtering recommendation algorithm, this paper proposes User-CF algorithm, User-CF-1 algorithm, Item-CF algorithm, Item-CF-1 algorithm, and finally integrates four algorithms to obtain the cooperative strategy based on collaborative filtering is a hybrid recommendation algorithm, namely the Final algorithm. It ha

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Zhao, Yanchi, Jianhua Cheng, and Jing Cai. "Improved Brain Storm Optimization Algorithm Based on Flock Decision Mutation Strategy." Algorithms 17, no. 5 (2024): 172. http://dx.doi.org/10.3390/a17050172.

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To tackle the problem of the brain storm optimization (BSO) algorithm’s suboptimal capability for avoiding local optima, which contributes to its inadequate optimization precision, we developed a flock decision mutation approach that substantially enhances the efficacy of the BSO algorithm. Furthermore, to solve the problem of insufficient BSO algorithm population diversity, we introduced a strategy that utilizes the good point set to enhance the initial population’s quality. Simultaneously, we substituted the K-means clustering approach with spectral clustering to improve the clustering accur

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Kenechukwu, Nwosu Ifeanyi, Kene Tochukwu Anyachebelu, and Muhammad, Umar Abdullahi. "Detection of Fraudulent Health Insurance Claims Based on Decision Tree with Principal Component Analysis." Asian Journal of Research in Computer Science 16, no. 4 (2023): 49–66. http://dx.doi.org/10.9734/ajrcos/2023/v16i4370.

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Fraudulent health insurance claims pose a significant challenge to insurance companies and healthcare providers, leading to substantial financial losses and compromised service quality. In this study, we focused on detecting fraudulent health insurance claims using the decision tree algorithm and principal component analysis (PCA). The objective was to gain valuable insights and extract meaningful patterns from the dataset to enhance fraud detection capabilities. We developed a comprehensive method that employed the decision tree algorithm to build a decision tree-based model and the PCA for d

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Priyanka, Saini. "Decision Tree Algorithm Implementation Using Educational Data." International Journal of Computer-Aided technologies (IJCAx) 1, April (2021): 31–41. https://doi.org/10.5281/zenodo.5105645.

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There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.

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Priyanka, Saini1 Sweta Rai2 and Ajit Kumar Jain3 1. 2. M.Tech Student Banasthali University Tonk Rajasthan. "Decision Tree Algorithm Implementation Using Educational Data." International Journal of Computer-Aided technologies (IJCAx) 01, dec (2014): 01–11. https://doi.org/10.5281/zenodo.1450276.

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There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.

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Zheng, Xin, and Ai Ping Cai. "Image Fusion Based on Shearlet and Multi-Decision." Advanced Materials Research 889-890 (February 2014): 1103–6. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.1103.

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Image Fusion is an important and useful subject in Image Processing and Computer Vision. The traditional image fusion algorithm could not provide satisfactory fusion results. Aiming to solving this problem, in this paper, we proposed an algorithm based on shearlet and multi-decision. First we discussed the application of the shearlet transform. Then we use difference decision rules for image decomposition high-frequency coefficients. Finally, the fused image is obtained through inverse Shearlet transform. Experimental results show that comparing with traditional image fusion algorithms, the pr

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Panggabean, Erwin, Agustina Simangunsong, Dedi Sinaga, Agus Putra Emas Sihombing, and Tri Evalina Aritonang. "Comparison of the K Mean Algorithm with the C 45 Algorithm in Dataming Applications." Journal of Computer Networks, Architecture and High Performance Computing 7, no. 1 (2025): 181–89. https://doi.org/10.47709/cnahpc.v7i1.5319.

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This research topic discusses the comparison of the K-Means and C4.5 algorithms in the application of data mining to predict aquarium sales in a company. K-Means is a clustering algorithm that functions to group data based on similarity, for example grouping customers based on frequency or type of purchase. This helps companies understand market segments and design marketing strategies accordingly. Meanwhile, C4.5 is a classification algorithm that builds decision trees based on important attributes that influence sales, such as price, season, or promotions. This algorithm is able to predict s

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Wang, Jian, Hongxiang Wang, Mingzhe Fei, and Gang Zhou. "Vehicle Lane Changing Game Model Based on Improved SVM Algorithm." World Electric Vehicle Journal 15, no. 11 (2024): 505. http://dx.doi.org/10.3390/wevj15110505.

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In order to improve the autonomous lane-changing performance of unmanned vehicles, this paper aims to solve the problem of inaccurate decision classification in traditional support vector machine (SVM) algorithms applied to the lane-changing decision-making stage of intelligent driving vehicles. By using game theory-related theories and combining the improved support vector machine (SSA-SVM) method, a vehicle autonomous lane-changing strategy based on game theory is established. The optimized SVM method has certain advantages for vehicle lane-changing decision-making with a small sample size i

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Yu, Shuang, Xiongfei Li, Hancheng Wang, Xiaoli Zhang, and Shiping Chen. "C_CART: An instance confidence-based decision tree algorithm for classification." Intelligent Data Analysis 25, no. 4 (2021): 929–48. http://dx.doi.org/10.3233/ida-205361.

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In classification, a decision tree is a common model due to its simple structure and easy understanding. Most of decision tree algorithms assume all instances in a dataset have the same degree of confidence, so they use the same generation and pruning strategies for all training instances. In fact, the instances with greater degree of confidence are more useful than the ones with lower degree of confidence in the same dataset. Therefore, the instances should be treated discriminately according to their corresponding confidence degrees when training classifiers. In this paper, we investigate th

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Lv, Sheng. "Real Estate Marketing Adaptive Decision-Making Algorithm Based on Big Data Analysis." Security and Communication Networks 2022 (April 12, 2022): 1–11. http://dx.doi.org/10.1155/2022/3443182.

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Aiming at the problems of low stability and efficiency of marketing decision-making and large complexity of marketing decision-making in the current marketing adaptive decision-making algorithm, a real estate marketing adaptive decision-making algorithm based on Big Data analysis is proposed. By analyzing the concept of Big Data, using the Big Data distributed computing architecture, researching the data mining-related algorithms. By constructing an association rule algorithm, mining the rules between real estate marketing and related factors. Based on the Spark-distributed computing platform,

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López-Morales, Virgilio, and Joel Suárez-Cansino. "Reliable Intervals Method in Decision-Based Support Models for Group Decision-Making." International Journal of Information Technology & Decision Making 16, no. 01 (2017): 183–204. http://dx.doi.org/10.1142/s0219622016500498.

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In this paper, a methodology to derive reliable intervals for multiplicative preference relations (or pairwise comparison matrices) satisfying consistency and consensus indexes is introduced. Our approach is proposed via a combination of numerical algorithms and a nonlinear optimization algorithm. A synthesis of reliable intervals is achieved, where group decision makers show evidence of these intervals to express flexibility in the manner of their preferences, while accomplishing some a priori decision targets, rules and advice given by their current framework. The algorithms are applied to s

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Sun, Jin, and Yi Zhang. "A Communication Probability-Based Mapping Algorithm for Mesh-Based Network-on-Chip Systems." Journal of Circuits, Systems and Computers 27, no. 14 (2018): 1850226. http://dx.doi.org/10.1142/s0218126618502262.

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Network-on-chip (NoC) mapping algorithms significantly affect NoC system performance in terms of communication cost and energy consumption. For a specific application represented by a task graph, this paper proposes an energy-efficient mapping algorithm that searches for the mapping decision with best communication locality and therefore lowest energy consumption. To this end, we formulate the concerned mapping problem as an optimization model, and propose an effective meta-heuristic algorithm to solve the formulated optimization model. During the mapping procedure, we employ a simulation-free

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Sadhu, Tithli, Somanth Chowdhury, Shubham Mondal, Jagannath Roy, Jitamanyu Chakrabarty, and Sandip Kumar Lahiri. "A comparative study of metaheuristics algorithms based on their performance of complex benchmark problems." Decision Making: Applications in Management and Engineering 6, no. 1 (2023): 341–64. http://dx.doi.org/10.31181/dmame0306102022r.

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Metaheuristic approaches with extremely important improvements are very promising in the solution of intractable optimization problems. The objective of the present study is to test the capability of applications and compare the performance of the four selected algorithms from “classical” (simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE)) and “new generation” (firefly algorithm (FFA), krill herd (KH), grey wolf optimization (GWO), and symbiotic organism search (SOS)) each by solving selected benchmark problems that are used in

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Kalech, Meir, and Shulamit Reches. "Decision Making with Dynamic Uncertain Events." Journal of Artificial Intelligence Research 54 (November 1, 2015): 233–75. http://dx.doi.org/10.1613/jair.4869.

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When to make a decision is a key question in decision making problems characterized by uncertainty. In this paper we deal with decision making in environments where information arrives dynamically. We address the tradeoff between waiting and stopping strategies. On the one hand, waiting to obtain more information reduces uncertainty, but it comes with a cost. Stopping and making a decision based on an expected utility reduces the cost of waiting, but the decision is based on uncertain information. We propose an optimal algorithm and two approximation algorithms. We prove that one approximation

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Liu, Yishu, and Jun Li. "Brand Marketing Decision Support System Based on Computer Vision and Parallel Computing." Wireless Communications and Mobile Computing 2022 (March 30, 2022): 1–14. http://dx.doi.org/10.1155/2022/7416106.

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With the rapid development of information technology, decision support systems that can assist business managers in making scientific decisions have become the focus of research. At present, there are not many related studies, but from the brand marketing level, there are not many studies combining smart technology. Based on computer vision technology and parallel computing algorithms, this paper launches an in-depth study of brand marketing decision support systems. First, use computer vision technology and Viola-Jones face detection framework to detect consumers’ faces, and use the classic c

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Hong, Sen, Han Wu, Xiujuan Xu, and Wei Xiong. "Early Warning of Enterprise Financial Risk Based on Decision Tree Algorithm." Computational Intelligence and Neuroscience 2022 (July 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/9182099.

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To improve enterprise financial early warning, we propose an algorithm based on a decision tree. According to the shortcomings and defects of the classical algorithm and the traditional decision tree algorithm, in the ordinary decision tree improved algorithm based on PCA, there is a problem that the representativeness of the data after dimensionality reduction processing are not high, resulting in the fact that the accuracy of the algorithm can be improved slightly after multiple data runs. Based on the classical algorithm, attribute eigenvalues before classification are extracted twice, and

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Gao, Wen, Rong Yu, Zhaolei Yu, Zhuang Ma, and Md Masum. "Auxiliary Diagnosis Method of Chest Pain Based on Machine Learning." International Journal of Engineering and Technology 14, no. 4 (2022): 79–83. http://dx.doi.org/10.7763/ijet.2022.v14.1207.

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Chest pain is sudden, its pathological causes are complex and various, fatal or non-fatal so that improving the diagnostic accuracy is extremely important in the emergency system of prehospital and hospitals. Therefore, we propose a method of introducing a decision tree, support vector machine, and KNN algorithm in machine learning into the auxiliary diagnosis of chest pain. First select the algorithm with better performance among decision tree, support vector machine, and KNN algorithm; Then compare the classification performance of the CART algorithm, the support vector machine using the Gau

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Mr. A. Kuppuswamy, Mr A. Kuppuswamy, Ms P. Mahalakshmi, Ms S. Ranjani, and Ms K. Aruna. "Smart IOT Based Flood Detection and Alerting System Using Decision Tree Algorithm." International Journal of Research Publication and Reviews 6, no. 3 (2025): 5593–97. https://doi.org/10.55248/gengpi.6.0325.1267.

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Shu, Wen Hao, Zhang Yan Xu, and Shen Ruan. "A Quick Attribution Reduction Algorithm Based on Incomplete Decision Table." Advanced Materials Research 171-172 (December 2010): 154–58. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.154.

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At present, some scholars have provided the attribution reduction algorithms of incomplete decision table. The time complexity of many algorithms are .To cut down the time complexity of the algorithms for computing attribution reduction , the definition of discernibility matrix based on positive region and the corresponding definition of the attribution reduction are provided. At the same time, it is proved that the attribution reduction is equivalent to the attribution reduction based on the positive region. The discernibility matrix is simplified for not comparing the objects between .On thi

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Wu, Di, Sheng Yao Yang, and J. C. Liu. "Cognitive Radio Decision Engine Based on Multi-Objective Genetic Algorithm." Applied Mechanics and Materials 48-49 (February 2011): 314–17. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.314.

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The performance optimization of cognitive radio is a multi-objective optimization problem. Existing genetic algorithms are difficult to assign the weight of each objective when the linear weighting method is used to simplify the multi-objective optimization problem into a single objective optimization problem. In this paper, we propose a new cognitive decision engine algorithm using multi-objective genetic algorithm with population adaptation. A multicarrier system is used for simulation analysis, and experimental results show that the proposed algorithm is effective and meets the real-time re

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Mechernene, Amin, Vincent Judalet, Ahmed Chaibet, and Moussa Boukhnifer. "Detection and Risk Analysis with Lane-Changing Decision Algorithms for Autonomous Vehicles." Sensors 22, no. 21 (2022): 8148. http://dx.doi.org/10.3390/s22218148.

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Despite the great technological advances in ADAS, autonomous driving still faces many challenges. Among them is improving decision-making algorithms so that vehicles can make the right decision inspired by human driving. Not only must these decisions ensure the safety of the car occupants and the other road users, but they have to be understandable by them. This article focuses on decision-making algorithms for autonomous vehicles, specifically for lane changing on highways and sub-urban roads. The challenge to overcome is to develop a decision-making algorithm that combines fidelity to human

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Shayea, Ibraheem, Mahamod Ismail, Rosdiadee Nordin, and Hafizal Mohamad. "Adaptive Handover Decision Algorithm Based on Multi-Influence Factors through Carrier Aggregation Implementation in LTE-Advanced System." Journal of Computer Networks and Communications 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/739504.

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Although Long Term Evolution Advanced (LTE-Advanced) system has benefited from Carrier Aggregation (CA) technology, the advent of CA technology has increased handover scenario probability through user mobility. That leads to a user’s throughput degradation and its outage probability. Therefore, a handover decision algorithm must be designed properly in order to contribute effectively for reducing this phenomenon. In this paper, Multi-Influence Factors for Adaptive Handover Decision Algorithm (MIF-AHODA) have been proposed through CA implementation in LTE-Advanced system. MIF-AHODA adaptively m

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Sun, Shuo, and Qi Zhu. "A Joint Optimization Algorithm for UAV Location and Offloading Decision Based on Wireless Power Supply." Electronics 13, no. 12 (2024): 2320. http://dx.doi.org/10.3390/electronics13122320.

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In this paper, a joint optimization algorithm of offloading decision, energy harvesting time, and unmanned aerial vehicle (UAV) location is proposed for user equipment (UEs)’s task completion latency problem in a communication–sensing–computing integration scenario with wireless energy supply. Under the constraints of causality of energy harvesting consumption by the UEs and conditional mutual information, the total latency minimization problem of the UEs is established. Firstly, the optimization variables of the problem are transformed from three variables of offloading decision, energy harve

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Song, Qing Yang, Xun Li, Shu Yu Ding, and Zhao Long Ning. "A Markov-Based Multi-Attribute Vertical Handoff Decision Algorithm." Advanced Materials Research 785-786 (September 2013): 1403–7. http://dx.doi.org/10.4028/www.scientific.net/amr.785-786.1403.

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Many vertical handoff decision algorithms have not considered the impact of call dropping during the vertical handoff decision process. Besides, most of current multi-attribute vertical handoff algorithms cannot predict users’ specific circumstances dynamically. In this paper, we formulate the vertical handoff decision problem as a Markov decision process, with the objective of maximizing the expected total reward during the handoff procedure. A reward function is formulated to assess the service quality during each connection. The G1 and entropy methods are applied in an iterative way, by whi

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NIKDEL, ZAHRA, and HAMID BEIGY. "A GENETIC PROGRAMMING-BASED LEARNING ALGORITHMS FOR PRUNING COST-SENSITIVE CLASSIFIERS." International Journal of Computational Intelligence and Applications 11, no. 02 (2012): 1250011. http://dx.doi.org/10.1142/s1469026812500113.

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In this paper, we introduce a new hybrid learning algorithm, called DTGP, to construct cost-sensitive classifiers. This algorithm uses a decision tree as its basic classifier and the constructed decision tree will be pruned by a genetic programming algorithm using a fitness function that is sensitive to misclassification costs. The proposed learning algorithm has been examined through six cost-sensitive problems. The experimental results show that the proposed learning algorithm outperforms in comparison to some other known learning algorithms like C4.5 or naïve Bayesian.

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Lo, Win-Tsung, Yue-Shan Chang, Ruey-Kai Sheu, Chun-Chieh Chiu, and Shyan-Ming Yuan. "CUDT: A CUDA Based Decision Tree Algorithm." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/745640.

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Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in the era without new technology help. In order to improve data processing latency in huge data mining, in this paper, we design and implement a new parallelized decision tree algorithm on a CUDA (compute unified device architectu

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Modi, Ashish, Sharath Kumar J, Sharath Kumar J, et al. "EMPLOYEE PERFORMANCE APPRAISAL SYSTEM BASED ON RANKING AND REVIEWS." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (2017): 495. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.23489.

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Objective: In many organizations, employee data have to be maintained and utilized for many purposes. Here, in this paper, we are going to use such data to calculate an employee’s performance.Methods: This employee data may be converted into useful information using data mining techniques such as K-means and decisions tree. K-means is used to find the rank of the employee means that the employee may come under in his criteria. Decision tree is used to find the review of an employee means that the employee needs improvement or he/she meets expectation.Results: This algorithm when utilized can i

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Duan, Huajie, Zhengdong Deng, Feifan Deng, and Daqing Wang. "Assessment of Groundwater Potential Based on Multicriteria Decision Making Model and Decision Tree Algorithms." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/2064575.

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Groundwater plays an important role in global climate change and satisfying human needs. In the study, RS (remote sensing) and GIS (geographic information system) were utilized to generate five thematic layers, lithology, lineament density, topology, slope, and river density considered as factors influencing the groundwater potential. Then, the multicriteria decision model (MCDM) was integrated with C5.0 and CART, respectively, to generate the decision tree with 80 surveyed tube wells divided into four classes on the basis of the yield. To test the precision of the decision tree algorithms, th

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Jia, Xianguang, Xinbo Zhou, Jing Bao, Guangyi Zhai, and Rong Yan. "Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes." Applied Sciences 13, no. 3 (2023): 1497. http://dx.doi.org/10.3390/app13031497.

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To solve the decision problem of train stopping schemes, this paper introduces the static game into the optimal configuration of stopping time to realize the rational decision of train operation. First, a train energy consumption model is constructed with the lowest energy consumption of train operation as the optimization objective. In addition, a Mustang optimization algorithm based on cubic chaos mapping, the population hierarchy mechanism, the golden sine strategy, and the Levy flight strategy was designed for solving the problem of it being easy for the traditional population intelligence

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Hindenberger, Patric, and Volker Schwieger. "Dynamic Location Referencing: Probability-Based Decision System." Advances in Cartography and GIScience of the ICA 2 (November 6, 2019): 1–8. http://dx.doi.org/10.5194/ica-adv-2-6-2019.

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Abstract. Location Referencing is a well-known methodology to transfer geoobjects from one digital map to another and typically used to share traffic information. Here, especially the dynamic methods play a major role, as they are developed to transfer Location References between different maps in such cases where no common databases and/or common structures are available. The key issue in dynamic Location Referencing is to find the correct geoobject in the target map which corresponds to the geoobject in the source map. So far, in nearly all methods a deterministic algorithm is implemented to

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