Traditional academic-based student management fails to meet the demands of high-cost experimental disciplines such as biotechnology. This study presents Business Decision Tree-Plus, an intelligent decision-support system for precise, cost-effective student interventions. Its enhanced decision tree incorporates business-inspired revenue-cost trade-offs into node-splitting criteria, balancing predictive accuracy with resource efficiency. Leveraging a multi-source data lake and dual-loop feedback framework covering the student lifecycle, the system enables real-time strategy adjustments based on industry feedback and academic performance. Empirical tests achieved 0.87 accuracy and 0.84 recall. A 12-week intervention trial demonstrated a 33.3% higher return on investment, significantly fewer laboratory accidents, and improved employer satisfaction. These findings establish a closed-loop decision-support paradigm offering sustainable management for high-cost experimental disciplines in higher education.
In the digital era, traditional physical education evaluation methods—often paper-based, subjective, and delayed—fail to support timely and evidence-based instructional decisions. To address this gap, an intelligent decision support system (IDSS) tailored for physical education evaluation in smart educational environments was proposed. The system integrated a neural network for data processing, an expert system for rule-based reasoning, and a multi-criteria autonomous advantage model to identify individual strengths and improvement areas. It integrated multisource student performance and behavioral signals collected from digital educational environments to generate dynamic and personalized evaluation results. Through system analysis and empirical testing, the proposed IDSS is shown to have the potential to enhance objectivity, reduce evaluator bias, and support teaching and learning decisions. This work contributes a practical IDSS framework for educational applications, aligning with the evolving needs of intelligent, data-driven decision support.
To address deficiencies of traditional parameter optimization methods in Tunnel Boring Machine construction of extremely hard rock tunnels, this study proposes an intelligent decision support system based on the fusion of convolutional neural network and long short-term memory with the Lingxi extremely hard rock tunnel as the engineering background. Integrating multisource real-time monitoring data, the system adopts a multiloop closed-loop feedback mechanism and a Decision Gate module for dynamic model adjustment combined with composite loss functions to enhance stability. Comparative experiments show the fusion model outperforms traditional models in prediction accuracy and antinoise ability, effectively supporting real-time parameter optimization and early warning of risks in complex tunnel construction. Its practical value is verified in engineering, providing a new technical path for integrating big data and artificial intelligence models in tunnel engineering.
The evaluation of teaching quality in college English translation courses refines pedagogical methods, elevates teaching efficiency, guides learning, and guarantees the development of practical translation talents that meet social demands. Teaching quality evaluation in college English translation courses constitutes a multiple-attribute group decision-making (MAGDM) challenge. Contemporary approaches have increasingly adopted the Logarithmic TODIM technique to address such complex MAGDM scenarios. Given the inherent uncertainties in teaching quality evaluation in college English translation courses, single-valued neutrosophic sets (SVNSs) serve as an effective framework for representing ambiguous data. This research develops an integrated single-valued neutrosophic number (SVNN)-LogTODIM technique specifically designed to handle MAGDM problems within neutrosophic environments. To demonstrate the practical application and effectiveness of the proposed framework, the study presents a detailed numerical example focusing on teaching quality evaluation in college English translation courses.
Efficiently managing the complex information flow from music score to high-fidelity performance is a key challenge in improving creative efficiency. Traditional methods often treat arrangement and performance as independent links, creating information separation and a semantic gap. This paper proposes and implements an artificial intelligence strategy-driven piano score allocator and performance-generation system, centered on a multi-level information-processing architecture with closed-loop feedback. By constructing a closed-loop feedback system of arrangement, performance, man, and machine, the system enables cross-level information feedback and real-time mutual error correction, effectively bridging semantic faults between note layout and dynamic rendering. This study provides a high-performance piano score allocator and automatic performance generation strategy, and its multi-level, closed-loop information-processing paradigm offers a repeatable engineering framework for designing and managing intelligent content-generation systems.
