Volume-12, Issue-8, August 2026

Abstract

Accurate house price prediction is of great significance for homebuyers, developers, and policymakers, yet the complexity and non-linearity of housing markets pose substantial challenges to traditional linear models. This study addresses the dual goals of high predictive accuracy and high interpretability by systematically comparing five regression models—linear regression, random forest, XGBoost, LightGBM, and multilayer perceptron (MLP)—on a large-scale second-hand housing dataset from Nanjing, China. Model performance is evaluated using MAE, RMSE, and R². Results show that ensemble tree models significantly outperform linear regression and MLP, with LightGBM and XGBoost achieving the lowest MAE (6,200 RMB/m²) and RMSE (8,800 RMB/m²), and LightGBM attaining the highest R² (0.92). We further employ SHAP (SHapley Additive exPlanations) to open the "black box" of the optimal LightGBM model. Global feature importance reveals that house age, subway distance, and decoration condition are the three most influential drivers of Nanjing house prices. SHAP summary plots demonstrate that newer houses, closer subway proximity, and better decoration consistently raise prices, while area exhibits a non-monotonic effect—small units command higher unit prices under total-price constraints, whereas very large luxury properties show diminishing marginal value. Prediction diagnostics confirm robust performance in the mainstream price range (15,000–35,000 RMB/m²), though extreme high-end properties remain harder to predict due to unobserved idiosyncratic factors. This study not only provides a high-performance predictive tool for the Nanjing market but also offers transparent, actionable insights into the underlying price-formation mechanism. The integrated framework of machine learning plus SHAP interpretability is readily generalisable to other cities and real-estate contexts, supporting evidence-based decision-making and policy design.

Keywords: House price prediction; Machine learning

References

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Abstract

Enterprise Resource Planning (ERP) systems have become indispensable for integrating organizational processes and improving operational efficiency. However, evaluating ERP system effectiveness remains challenging because conventional evaluation methods often rely on subjective assessment techniques and fail to capture the complex relationships among multiple organizational and operational performance indicators. This study proposes a hybrid framework that integrates Inverse Variance Weighting (IVW), Tabu Search Algorithm (TSA), and supervised Machine Learning (ML) models for objective ERP effectiveness evaluation. Operational and organizational Key Performance Indicators (KPIs), including throughput, lead time, defect rate, machine utilization, system uptime, response time, employee productivity, order fulfillment rate, supply chain responsiveness, customer satisfaction, cost efficiency, and return on investment, were employed to construct a composite ERP effectiveness index. The IVW technique objectively assigned KPI weights based on statistical stability, while Tabu Search optimized KPI selection and feature combinations. Linear Regression, Support Vector Regression, and Random Forest Regression models were subsequently trained to predict ERP effectiveness. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), coefficient of determination (R²), and five-fold cross-validation. The proposed framework provides an objective, scalable, and intelligent decision-support mechanism capable of improving ERP performance evaluation in manufacturing environments. The study contributes a novel hybrid optimization and machine learning framework that enhances predictive accuracy while reducing the subjectivity associated with conventional ERP evaluation techniques.

Keywords: Enterprise Resource Planning, ERP Effectiveness, Inverse Variance Weighting, Tabu Search, Machine Learning, Key Performance Indicators.

References

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Abstract

The digital transformation of the wood-processing industry increasingly relies on computer vision, artificial intelligence (AI), and embedded sensing technologies to automate timber quality assessment. This study presents the laboratory validation (TRL4) of a low-cost embedded computer-vision demonstrator for automated surface-defect detection in beech (Fagus sylvatica) sawn timber, developed as the first operational module of the SMARTWOOD-AI technology-transfer platform. The system integrates a Raspberry Pi 4 single-board computer with a Sony IMX500 intelligent camera and employs an interpretable computer-vision pipeline based on 21 handcrafted colour (HSV), texture (Gray-Level Co-occurrence Matrix and Local Binary Patterns), and edge-density (Canny) features classified using a Random Forest algorithm. A dataset comprising 24 beech boards (192 labelled image regions) was evaluated using a board-level train/validation partitioning strategy to prevent data leakage. The classifier achieved an average accuracy of 88.2% (±7.1%) during five-fold cross-validation and 82.5% accuracy on an independent validation set, with high sensitivity for defect detection (recall = 0.93, F1-score = 0.88). The results demonstrate the technical feasibility of the proposed embedded inspection architecture and establish a reproducible experimental baseline for future integration of deep-learning models, digital twins, and intelligent cutting optimisation within the SMARTWOOD-AI platform.

Keywords: wood defect detection; embedded computer vision; on-sensor AI; Random Forest; Technology Readiness Level (TRL4); beech sawn timber; digital twin; edge artificial intelligence; Industry 4.0; smart sawmill.

References

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Abstract

This research addresses the low availability of critical equipment in a physiological serum production line, characterized by frequent unscheduled shutdowns. The objective was to propose a plan to improve the availability of critical equipment, specifically in the serum filling machine, identified through a criticality and complexity analysis based on mathematical models and expert criteria. Key performance indicators such as reliability, maintainability and availability were evaluated. Initial results showed limited reliability, a high mean time to repair (10.02 h), and an availability of 89.4%, lower than the desired standard (>95%). 60% of the failures were mechanical in nature, which oriented the analysis towards the identification of causes using tools such as the Ishikawa diagram. Based on the opportunities detected, a comprehensive plan of corrective actions was designed, which included the improvement of maintenance procedures, staff training, optimization of spare parts management, implementation of condition monitoring and technological updating. The partial application of the plan showed positive results: reduction of the average repair time to 5.56 h and increase in availability to 93.59%, as well as significant improvements in the mechanical reliability of the equipment. It is concluded that the systematic implementation of the proposed plan contributes to improving the operational performance of critical equipment, increasing production efficiency and reducing economic losses in the physiological serum production line.

Keywords: Availability Improvement, Reliability and Maintainability Analysis, Critical Equipment Management, Industrial Maintenance, Failure Analysis.

References

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Abstract

Thermal power plant performance depends strongly on coupled control variables whose effects extend simultaneously to power output, fuel use, heat rate, efficiency, emissions, and operating economics. This study develops a hybrid multi-criteria simulation model by reorganizing a simulation-based Digital Twin into an integrated engineering decision-support framework. The model combines first-principles representations of the boiler, steam turbine, condenser and feedwater pump with deterministic one-second time-step simulation, fourth-order Runge–Kutta integration, metaheuristic optimization modules based on Particle Swarm Optimization and Genetic Algorithm concepts, and technical, environmental, economic and grid-related performance criteria. A 24 h operating cycle generated 86,400 time-stamped simulation states. Reported output ranged from 147.9 to 217.5 MW, with an average of 190.4 MW. Mean thermal efficiency and heat rate were 41.8% and 8,608 kJ/kWh, respectively, while total generated energy was 4,568.5 MWh. The simulation consumed 378.0 t of fuel and emitted 1,039.5 t of CO₂, corresponding to approximately 0.228 kg CO₂/kWh. Total revenue and gross operating profit were $296,953.27 and $43,315.27 over the simulated day. Computational validation maintained per-step energy-balance error at or below 0.01%, and the thesis reports benchmark deviation below 1.5% over the stated load range. The principal contribution is the integration of plant-wide thermodynamic simulation with multi-domain performance assessment and an optimization-ready control layer. Because the source study does not report complete optimizer hyperparameters, convergence histories or final optimized decision vectors, no quantified PSO-versus-GA improvement is claimed. The framework is therefore positioned as a validated computational test bed for plant performance and control studies rather than a field-deployed Digital Twin. 

Keywords: Digital Twin; thermal power plant; heat rate; thermodynamic modelling; multi-criteria assessment; Particle Swarm Optimization; Genetic Algorithm; decision support.

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