Relevant bibliographies by topics / Basket Ranking Algorithm

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Basket Ranking Algorithm'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Basket Ranking Algorithm"

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Deep, Thakkar, Sonar Shalaka, and Bhangdia |. Digvijay Sonawane Niddhi. "Budget Management Shopping Strategy for an E Commerce Website." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 1573–76. https://doi.org/10.31142/ijtsrd11308.

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Recent e commerce website does not assure shopping within a stipulated budget. Many a times when our basket cart contains multiple products our budget gets collapsed. This somewhere affects customer shopping experience. Hence in this research we propose a methodology where a different combination of products, that customer wants to purchase is made. Then these combinations or these baskets are further evaluated based on customer's given budget. While suggesting readymade baskets to customer, we make sure that quality of product is not hampered. During these transactions it is verified that
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Ayushi, Nainwal* Arvind Kalia Jawahar Thakur. "NODE CATEGORISATION ALGORITHMS IN WIRELESS SENSOR NETWORK: A COMPARATIVE STUDY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 11 (2016): 510–17. https://doi.org/10.5281/zenodo.168436.

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Wireless sensor network are often installed in unattended environment for monitoring and sending information to base station. If nodes in the network are compromised then the security of the network degrades quickly. There have been many approaches researched to tackle this issue. This paper introduces three node categorisation algorithms named Global Ranking Algorithm, Stepwise Ranking Algorithm and Hybrid Ranking Algorithm, which can identify misbehaving forwarders that drop or modify packets. In network each packet is padded and encrypted so as to hide the source of the packet. The packet m
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Isha, Mahajan. "Extended Weighted Page Rank Based on VOL by Finding User Activities Time and Page Reading Time, Storing them Directly on Search Engine Database Server." International Journal of Engineering Works (ISSN:2409-2770) 4, no. 2 (2017): 41–48. https://doi.org/10.5281/zenodo.376487.

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Searching on the web can be considered as a process of user enters the query and search system returns a set of most relevant pages in response to user’s query. But results returned are not mostly relevant to user’s query and ranking of the pages are not efficient according to user requirement. In order to improve the precision of ranking of the web pages, after analyzing the different algorithms like Page Rank, Weighted Page Rank, Page Rank based on VOL, Weighted Page Rank algorithm based on VOL. In this paper, we are proposing enhancement by including “User Activities Time” and “Page Reading
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M, Muralidharan, and Jeyachidra J. "A Competent Information Retrieval in Medical Subject Heading Using Phrases Term Weightage based Ranking Algorithm." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (2019): 1016–23. http://dx.doi.org/10.5373/jardcs/v11/20192666.

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Sharma, Abhishek, Abhinav Sharma, Ankit Dasgotra, et al. "Opposition-Based Tunicate Swarm Algorithm for Parameter Optimization of Solar Cells." IEEE Access 9 (September 7, 2021): 125590–602. https://doi.org/10.1109/ACCESS.2021.3110849.

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Parameter estimation of photovoltaic modules is an essential step to observe, analyze, and optimize the performance of solar power systems. An efficient optimization approach is needed to obtain the finest value of unknown parameters. Herewith, this article proposes a novel opposition-based tunicate swarm algorithm for parameter estimation. The proposed algorithm is developed based on the exploration and exploitation components of the tunicate swarm algorithm. The opposition-based learning mechanism is employed to improve the diversification of the search space to provide a precise solution. T
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Yaseen, Nauman. "Predicting stock dividend using neural network and decision tree and comparing them with voting technique." Journal of Management and Accounting Studies 6, no. 03 (2020): 60–63. http://dx.doi.org/10.24200/jmas.vol6iss03pp60-63.

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Data mining is one of the developing sciences and it is very suitable for analyzing database. Data mining is used in many sciences, such as business intelligence, shopping basket analysis, and medicine. The main algorithms of data mining are 4 categories that 2 main categories are feature ranking and classification algorithms. In this study, we propose a method for predicting the dividend of market price using data mining technique. A new method has been provided for classifying the data, and then the accuracy of each method has been achieved by implementing the above approach on a database wi
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Dr., (Mrs.) Pushpa R. Suri, and Taneja Harmunish. "AN INTEGRATED RANKING ALGORITHM FOR EFFICIENT INFORMATION COMPUTING IN SOCIAL NETWORKS." International Journal on Web Service Computing (IJWSC) 3, no. 1 (2012): 31–44. https://doi.org/10.5281/zenodo.4072585.

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Social networks have ensured the expanding disproportion between the face of WWW stored traditionally in search engine repositories and the actual ever changing face of Web. Exponential growth of web users and the ease with which they can upload contents on web highlights the need of content controls on material published on the web. As definition of search is changing, socially-enhanced interactive search methodologies are the need of the hour. Ranking is pivotal for efficient web search as the search performance mainly depends upon the ranking results. In this paper new integrated ranking mo
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Dr., (Mrs.) Pushpa R. Suri, and Taneja Harmunish. "AN INTEGRATED RANKING ALGORITHM FOR EFFICIENT INFORMATION COMPUTING IN SOCIAL NETWORKS." International Journal on Web Service Computing (IJWSC) 3, no. 1 (2012): 31–44. https://doi.org/10.5281/zenodo.3460082.

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Social networks have ensured the expanding disproportion between the face of WWW stored traditionally in search engine repositories and the actual ever changing face of Web. Exponential growth of web users and the ease with which they can upload contents on web highlights the need of content controls on material published on the web. As definition of search is changing, socially-enhanced interactive search methodologies are the need of the hour. Ranking is pivotal for efficient web search as the search performance mainly depends upon the ranking results. In this paper new integrated ranking mo
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R. J. Kuo, R. J. Kuo, and Zhen Wu R. J. Kuo. "Applying Evolutionary-based User Characteristic Clustering and Matrix Factorization to Collaborative Filtering for Recommender Systems." 網際網路技術學刊 23, no. 4 (2022): 693–708. http://dx.doi.org/10.53106/160792642022072304005.

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<p>In recent years, with the rise of numerous Internet service industries, recommender systems have been widely used as never before. Users can easily obtain the information, products or services they need from the Internet, and businesses can also increase additional revenue through the recommender system. However, in today’s recommender system, the data scale is very large, and the sparsity of the scoring data seriously affects the quality of the recommendation. Thus, this study intends to propose a recommendation algorithm based on evolutionary algorithm, which combines user
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Paul, Aazagreyir, Appiahene Peter, Appiah Obed, and Boateng Samuel. "Comparative analysis of fuzzy multi-criteria decision-making methods for quality of service-based web service selection." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1408–19. https://doi.org/10.11591/ijai.v13.i2.pp1408-1419.

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This research aims to compare and analyze the effectiveness of four popular fuzzy multi-criteria decision-making methods (FMCDMMs) for quality of service (QoS)-based web service selection. These methods are fuzzy DEMATEL (FD), fuzzy TOPSIS (FT), fuzzy VIKOR (FV), and fuzzy PROMETHEE (FP), including three ranking versions of FV. We assess the ranking similarities among these methods using Spearman's relationship figure. We describe the algorithms of these six FMCDMs in the methods section. In a case study, we collected primary data from five experts who rated nine QoS factors of nine web servic
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Dissertations / Theses on the topic "Basket Ranking Algorithm"

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Bouchaud, Paul. "Beyond the Black Box. Social Structures and Dynamics in the Digital Age : Reconstructing, Modelling and Assessing the Impact of Major Digital Infrastructures." Electronic Thesis or Diss., Paris, EHESS, 2024. http://www.theses.fr/2024EHES0162.

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Cette thèse examine les effets des systèmes algorithmiques utilisés par les grandes plateformes en ligne sur le discours public et la société. À travers des audits expérimentaux et des simulations sociales, cette thèse vise à déchiffrer le fonctionnement de ces systèmes qui servent des milliards d'utilisateurs. La thèse aborde trois objectifs principaux : mener des audits des systèmes algorithmiques des plateformes en ligne, étudier les mesures adoptées par les plateformes pour atténuer les effects néfastes de leurs opérations sur la société, et améliorer les simulations sociales avec des donn
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Andrieu, Pierre. "Passage à l'échelle, propriétés et qualité des algorithmes de classements consensuels pour les données biologiques massives." Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG041.

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Les médecins et biologistes sont régulièrement amenés à interroger des bases de données biologiques publiques, par exemple lorsqu’ils se renseignent sur les gènes les plus associés à une maladie donnée. Le mot-clé choisi au moment d’interroger la base de données est particulièrement important : plusieurs reformulations synonymes d’une même maladie (par exemple « breast cancer » et « breast carcinoma ») aboutissent à des classements parfois très différents pouvant aller jusqu’à plusieurs milliers de gènes. Certains gènes, triés par pertinence, peuvent se retrouver à égalité (importance égale vi
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Kolluru, Ramesh. "Novel, Robust and Accurate Central Solvers for Real, Dense and Multicomponent Gases." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/4667.

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The nonlinear convection terms in the governing equations of inviscid compressible fluid flows are nontrivial for modelling and numerical simulation. The traditional Riemann solvers, which are strongly dependent on the underlying eigen-structure of the governing equations. Extension of the existing methods for generalized Equation of State (EOS), to incorporate real gas effects and for multicomponent fluids, is not straight forward as the eigen-structure can become complicated. Objective of the present work is to develop simple algorithms which are not dependent on the eigen-structur
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Book chapters on the topic "Basket Ranking Algorithm"

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Brazdil, Pavel, Jan N. van Rijn, Carlos Soares, and Joaquin Vanschoren. "Metalearning Approaches for Algorithm Selection I (Exploiting Rankings)." In Metalearning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67024-5_2.

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SummaryThis chapter discusses an approach to the problem of algorithm selection, which exploits the performance metadata of algorithms (workflows) on prior tasks to generate recommendations for a given target dataset. The recommendations are in the form of rankings of candidate algorithms. The methodology involves two phases. In the first one, rankings of algorithms/workflows are elaborated on the basis of historical performance data on different datasets. These are subsequently aggregated into a single ranking (e.g. average ranking). In the second phase, the average ranking is used to schedul
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Burke, Robin. "Ranking Algorithms for Costly Similarity Measures." In Case-Based Reasoning Research and Development. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44593-5_8.

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Kura, Satoshi, Hiroshi Unno, and Ichiro Hasuo. "Decision Tree Learning in CEGIS-Based Termination Analysis." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_4.

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AbstractWe present a novel decision tree-based synthesis algorithm of ranking functions for verifying program termination. Our algorithm is integrated into the workflow of CounterExample Guided Inductive Synthesis (CEGIS). CEGIS is an iterative learning model where, at each iteration, (1) a synthesizer synthesizes a candidate solution from the current examples, and (2) a validator accepts the candidate solution if it is correct, or rejects it providing counterexamples as part of the next examples. Our main novelty is in the design of a synthesizer: building on top of a usual decision tree lear
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Scott, Joseph, Aina Niemetz, Mathias Preiner, Saeed Nejati, and Vijay Ganesh. "MachSMT: A Machine Learning-based Algorithm Selector for SMT Solvers." In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72013-1_16.

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AbstractIn this paper, we present MachSMT, an algorithm selection tool for Satisfiability Modulo Theories (SMT) solvers. MachSMT supports the entirety of the SMT-LIB language. It employs machine learning (ML) methods to construct both empirical hardness models (EHMs) and pairwise ranking comparators (PWCs) over state-of-the-art SMT solvers. Given an SMT formula $$\mathcal {I}$$ I as input, MachSMT leverages these learnt models to output a ranking of solvers based on predicted run time on the formula $$\mathcal {I}$$ I . We evaluate MachSMT on the solvers, benchmarks, and data obtained from SMT
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Arya, Swasti, Mihika Chitranshi, and Yograj Singh. "Ranking Forecasting Algorithms Using MCDM Methods: A Python Based Application." In Computational Intelligence-based Time Series Analysis. River Publishers, 2022. http://dx.doi.org/10.1201/9781003337676-7.

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van Bakel, Ruud, Teodor Aleksiev, Daniel Daza, Dimitrios Alivanistos, and Michael Cochez. "Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72308-8_8.

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AbstractLarge, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes.Structured querying on such incomplete graphs will result in incomplete sets of answers, even if the correct entities exist in the graph, since one or more edges needed to match the pattern are missing.
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Busch, Christoph. "From Algorithmic Transparency to Algorithmic Choice: European Perspectives on Recommender Systems and Platform Regulation." In The International Library of Ethics, Law and Technology. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34804-4_3.

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AbstractAlgorithmic recommendations and rankings have become a key feature of the user experience offered by digital platforms. Recommender systems determine which information and options are prominently presented to users. While there is abundant technical literature on recommender systems, the topic has only recently attracted the attention of the European legislator. This chapter scrutinizes the emerging European regulatory framework for algorithmic rankings and recommendations in the platform economy with a specific focus on online retail platforms. Surveying the new rules for rankings and
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Cavenaghi, Emanuele, Lorenzo Camaione, Paolo Minasi, Gabriele Sottocornola, Fabio Stella, and Markus Zanker. "A Re-rank Algorithm for Online Hotel Search." In Information and Communication Technologies in Tourism 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25752-0_5.

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AbstractRecommender Systems were created to support users in situations of information overload. However, users are consciously or unconsciously influenced by several factors in their decision-making. We analysed a historical dataset from a meta-search booking platform with the aim of exploring how these factors influence user choices in the context of online hotel search and booking. Specifically, we focused our study on the influence of (i) ranking position, (ii) number of reviews, (iii) average ratings and (iv) price when analysing users’ click behaviour. Our results confirmed conventional
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Havlena, Vojtěch, Ondřej Lengál, and Barbora Šmahlíková. "Complementation of Emerson-Lei Automata." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90897-2_5.

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Abstract We give new constructions for complementing subclasses of Emerson-Lei automata using modifications of rank-based Büchi automata complementation. In particular, we propose a specialized rank-based construction for a Boolean combination of Inf acceptance conditions, which heavily relies on a novel way of a run DAG labelling enhancing the ranking functions with models of the acceptance condition. Moreover, we propose a technique for complementing generalized Rabin automata, which are structurally as concise as general Emerson-Lei automata (but can have a larger acceptance condition). The
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Tesfaye, Bezaye, Nikolaus Augsten, Mateusz Pawlik, Michael H. Böhlen, and Christian S. Jensen. "An Efficient Index for Reachability Queries in Public Transport Networks." In Advances in Databases and Information Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54832-2_5.

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AbstractComputing path queries such as the shortest path in public transport networks is challenging because the path costs between nodes change over time. A reachability query from a node at a given start time on such a network retrieves all points of interest (POIs) that are reachable within a given cost budget. Reachability queries are essential building blocks in many applications, for example, group recommendations, ranking spatial queries, or geomarketing. We propose an efficient solution for reachability queries in public transport networks. Currently, there are two options to solve rea
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Conference papers on the topic "Basket Ranking Algorithm"

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Yu, Jiazhao, Weigang Wu, and Jieying Zhou. "Improving Validity-guided Grey-box Fuzzing based on Adaptive Seed Ranking." In 2025 2nd International Conference on Algorithms, Software Engineering and Network Security (ASENS). IEEE, 2025. https://doi.org/10.1109/asens64990.2025.11011137.

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Hijazi, Sherin, Mahmoud Moshref, and Samer Mohammed Arqawi. "Evaluating Factors Influencing University Ranking Based on QS Ranking 2023-2024 Using Machine Learning Algorithms." In 2025 1st International Conference on Computational Intelligence Approaches and Applications (ICCIAA). IEEE, 2025. https://doi.org/10.1109/icciaa65327.2025.11013585.

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C, Iswarya, and Sreeharan B. N. "Assessing Indian Private Banks: A DEAR Algorithm-Based Ranking Method." In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). IEEE, 2025. https://doi.org/10.1109/icaeca63854.2025.11012328.

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Gorantla, Sruthi, Amit Deshpande, and Anand Louis. "Sampling Ex-Post Group-Fair Rankings." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/46.

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Randomized rankings have been of recent interest to achieve ex-ante fairer exposure and better robustness than deterministic rankings. We propose a set of natural axioms for randomized group-fair rankings and prove that there exists a unique distribution D that satisfies our axioms and is supported only over ex-post group-fair rankings, i.e., rankings that satisfy given lower and upper bounds on group-wise representation in the top-k ranks. Our problem formulation works even when there is implicit bias, incomplete relevance information, or only ordinal ranking is available instead of relevance
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Morik, Marco, Ashudeep Singh, Jessica Hong, and Thorsten Joachims. "Controlling Fairness and Bias in Dynamic Learning-to-Rank (Extended Abstract)." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/655.

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Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only do the users draw utility from the rankings, but the rankings also determine the utility (e.g. exposure, revenue) for the item providers (e.g. publishers, sellers, artists, studios). It has already been noted that myopically optimizing utility to the users -- as done by virtually all learning-to-rank algorithms -- can be unfair to the item providers. We, therefore, present a learning-to-rank approach for explicitly enforcing merit
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Chandrasekaran, Sowmya, and Thomas Bartz-Beielstein. "A Novel Ranking Scheme for the Performance Analysis of Stochastic Optimization Algorithms using the Principles of Severity." In 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024. KIT Scientific Publishing, 2024. https://doi.org/10.58895/ksp//1000174544-10.

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Stochastic optimization algorithms have been successfully applied in several domains to find optimal solutions. Because of the ever-growing complexity of integrated systems, novel stochastic algorithms are being proposed, which makes the task of the performance analysis of the algorithms extremely important. This paper provides a novel ranking scheme to rank the algorithms over multiple single-objective optimization problems. The results of the algorithms are compared using a robust bootstrapping-based hypothesis testing procedure that is based on the principles of severity. Analogous to the f
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Okada, César, and Daniel Carlos Pedronette. "Efficient Unsupervised Distance Learning through Rank Correlation Measures on Heterogeneous Systems." In XVII Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação - SBC, 2016. http://dx.doi.org/10.5753/wscad.2016.14246.

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The huge growth of image collections have demanded methods capable of conducting effective and efficient image searches. Among the most promising approaches, the Content-Based Image Retrieval (CBIR) systems have established as an alternative for automatically taking into account the visual information. Despite the important results achieved, retrieving relevant images (effectiveness) in minimal time (efficiency) remains a challenge task. Recently, unsupervised learning algorithms have been proposed to improve the effectiveness of CBIR systems by exploiting similarity and ranking information. Suc
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Pisani, Flávia, Daniel Pedronette, Ricardo Torres, and Edson Borin. "Improving the Performance of the Contextual Spaces Re-Ranking Algorithm on Heterogeneous Systems." In XVI Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação - SBC, 2015. http://dx.doi.org/10.5753/wscad.2015.14278.

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Re-ranking algorithms have been proposed to improve the effectiveness of Content-Based Image Retrieval (CBIR) systems by exploiting contextual information encoded in distance measures and ranked lists. In this paper, we show how we improved the efficiency of one of these algorithms, called Contextual Spaces Re-Ranking. We propose a modification to the algorithm that reduces its execution time by 1.6x on average and improves its accuracy in most of our test cases. We also parallelized the implementation with OpenCL to use the CPU and GPU of an Accelerated Processing Unit (APU). Employing these de
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Qin, Jing, Xinyue Liu, and Hongfei Lin. "Audio Retrieval Based on Manifold Ranking." In 2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP). IEEE, 2014. http://dx.doi.org/10.1109/paap.2014.14.

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Mazumder, Sahisnu, and Bing Liu. "Context-aware Path Ranking for Knowledge Base Completion." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/166.

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Knowledge base (KB) completion aims to infer missing facts from existing ones in a KB. Among various approaches, path ranking (PR) algorithms have received increasing attention in recent years. PR algorithms enumerate paths between entity-pairs in a KB and use those paths as features to train a model for missing fact prediction. Due to their good performances and high model interpretability, several methods have been proposed. However, most existing methods suffer from scalability (high RAM consumption) and feature explosion (trains on an exponentially large number of features) problems. This
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Reports on the topic "Basket Ranking Algorithm"

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Khrushch, Nila, Pavlo Hryhoruk, Tetiana Hovorushchenko, Sergii Lysenko, Liudmyla Prystupa, and Liudmyla Vahanova. Assessment of bank's financial security levels based on a comprehensive index using information technology. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4474.

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The article considers the issues of assessing the level of financial security of the bank. An analysis of existing approaches to solving this problem. A scientific and methodological approach based on the application of comprehensive assessment technology is proposed. The computational algorithm is presented in the form of a four-stage procedure, which contains the identification of the initial data set, their normalization, calculation of the partial composite indexes, and a comprehensive index of financial security. Results have interpretation. Determining the levels of financial security an
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