基于深度特征提取的混合元启发式集成学习模型用于启发式集成产量预测

Q3 Decision Sciences

International Journal of Information and Decision Sciences Pub Date : 1900-01-01 DOI:10.1504/ijids.2025.10052900

S. V. Bharathi, M. A.

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Novel Hybrid Meta-heuristic-enabled Ensemble Learning Model with Deep Feature Extraction for Crop Yield Prediction with Heuristic Ensemble Yield

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来源期刊

International Journal of Information and Decision Sciences Business, Management and Accounting-Management of Technology and Innovation

CiteScore

1.90

自引率

0.00%

发文量

期刊介绍： In today''s fast-paced business environment, even with an abundance of information, decision-making can be complex and slow. As floods of data emerge, effective information processing is sought as a panacea. With the ever-present spectre of uncertainty, sound decisions are key. As a consequence of the various conflicts/dilemmas, employment of efficient data management leading to better decision-making is the goal. Organisations must employ effective information management/decision-making processes at each critical stage of their functions. IJIDS addresses the issues involved in this. Topics covered include: -AHP or DEA -Behavioural sciences, psychology, sociology -Business intelligence, economics -Computing and decision sciences, data-driven decision making -Decision making in social setting, under uncertainty, with multimedia -Decision support systems/software -Decision theory, decision trees, MCDM -Ethical decision making, group decision making, software -Fuzzy information processing, game theory, grid analysis, informatics, IT -Intelligent agent technologies, neural networks, OLAP -Knowledge discovery in databases, web search, scenario/system analysis -Mathematics of decision sciences, decision making methods/styles -Perspectives of decision making, robust decisions, morphological analysis -Political/public decision making -Risk management, statistics