Nondestructive detection and classification of impurities-containing seed cotton based on hyperspectral imaging and one-dimensional convolutional neural network by Yeqi Fei, Zhenye Li, Tingting Zhu, Zengtao Chen, Chao Ni

“…With a processing time of 11.764 μs per pixel from experimental data, it shows a much improved speed requirement while maintaining the accuracy of real production lines. …”
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Dual-GRU Perception Accumulation Model for Linear Beam Smoke Detector by Zhuofu Wang, Boning Li, Li Wang, Zhen Cao, Xi Zhang

“…By improving the gated recurrent unit (GRU) neural network, an algorithm combining dual-wavelength information fusion and an attention mechanism was designed. …”
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Adaptive lift chiller units fault diagnosis model based on machine learning. by Yang Guo, Zengrui Tian, Hong Wang, Mengyao Chen, Pan Chu, Yingjie Sheng

“…In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. …”
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A multi-task genetic programming approach for online multi-objective container placement in heterogeneous cluster by Ruochen Liu, Haoyuan Lv, Ping Yang, Rongfang Wang

“…MOCP-MTGP can automatically generate multiple groups of allocation rules from historical workload patterns and different cluster states, and capture the similarities between all online tasks to guide the transfer of general knowledge during optimization. Comprehensive experiments show that the proposed algorithm can improve the resource utilization of clusters, reduce the number of physical machines, and effectively meet resource constraints and high availability requirements.…”
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Bilevel Programming Model of Urban Public Transport Network under Fairness Constraints by Jingjing Hao, Xinquan Liu, Xiaojing Shen, Nana Feng

“…It provides theoretical basis and model foundation for the optimization of public transit network, and it is a new attempt to improve the fairness of the traffic planning scheme.…”
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Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines by Hongbo Liu, Xiangzhao Meng

“…The development of this model contributes to improving the integrity management of oil and gas pipelines and provides decision support for the intelligent management of defective pipelines in oil and gas fields.…”
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An EfficientNet integrated ResNet deep network and explainable AI for breast lesion classification from ultrasound images by Kiran Jabeen, Muhammad Attique Khan, Ameer Hamza, Hussain Mobarak Albarakati, Shrooq Alsenan, Usman Tariq, Isaac Ofori

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Research on Efficient Coordination Control System of Virtual Coupling for Urban Rail Train by SHI Ke, WANG Yue, ZHANG Zhengfang, ZHOU Zhiwen, BAI Jinglei, LUO Qinyang

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Optimized Two-Stage Anomaly Detection and Recovery in Smart Grid Data Using Enhanced DeBERTa-v3 Verification System by Xiao Liao, Wei Cui, Min Zhang, Aiwu Zhang, Pan Hu

“…The recovery mechanism achieves remarkably precise restoration with a mean absolute error (MAE) of only 0.0055 kWh, representing a 99.91% improvement compared to traditional ARIMA models and 98.46% compared to standard Anomaly Transformer models. …”
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The Robust Steiner Team Orienteering Problem with Decreasing Priorities under budgeted uncertainty by Lucas Assunção, Andréa Cynthia Santos

“…The cut-and-branch algorithm based on the stronger model also stands out, solving 20 more instances than the other. …”
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An Empirical Taxonomy of Video Summarization Model From a Statistical Perspective by Sai Babu Veesam, Aravapalli Rama Satish

“…Some of the notable techniques include the SVS_MCO method using DBSCAN clustering optimized by the Artificial Algae Algorithm and the KDAN framework, which leverages knowledge distillation for supervised summarization. …”
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Multi-resource joint management strategy for 5 G network slicing based on POMDP by Yale Li

“…Such processes are capable of perceiving changes in network topology and dynamically adjusting resource allocation to adapt to constantly changing network conditions. Furthermore, the model employs a hybrid heuristic value iterative algorithm to optimize computational efficiency, reduce network latency, improve throughput, and enhance resource utilization. …”
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A bearing fault diagnosis method based on hybrid artificial intelligence models. by Lijie Sun, Xin Tao, Yanping Lu

“…The process employs Maximum Second-order Cyclostationary Blind Deconvolution (CYCBD) to filter out noise from the vibration signals emitted by bearings; secondly, considering the issue with the conventional Harris Hawks Optimization (HHO) algorithm which tends to prematurely converge to local optima, the differential evolution mutation operator is introduced and the escape energy factor is improved from linear to nonlinear in IHHO; then, a double-layer network model based on DBN-ELM is proposed, to avoid the number of hidden layer nodes of DBN from human experience interference, and IHHO is used to optimize DBN structure, which is denoted as IHHO-DBN-ELM method; with the optimal structure is obtained by using a combined IHHO optimized DBN and ELM; in conclusion, the proposed IHHO-DBN-ELM approach is applied to the bearing fault detection using the Western Reserve University's bearing fault dataset. …”
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Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing by Ahmet Hakan İnce, Serkan Özbay

“…Student abilities (θ), item difficulties (b), and time–effect parameters (λ) were estimated using the L-BFGS-B algorithm to ensure numerical stability. The results indicate that subtractive models, particularly DTA-IRT, achieved the lowest AIC/BIC values, highest AUC, and improved parameter stability, confirming their effectiveness in penalizing excessive response times without disproportionately affecting moderate-speed students. …”
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A Travel Demand Response Model in MaaS Based on Spatiotemporal Preference Clustering by Songyuan Xu, Yuqi Liang, Jing Zuo

“…To respond to travel demand in the MaaS system, improve transport efficiency, and optimize the framework of MaaS, we propose a travel demand response model based on a spatiotemporal preference clustering algorithm that considers the impact of travel preferences and features of the MaaS system to improve travel demand response and achieve full coverage of travel demands. …”
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Computational Linguistics Applications in AI-Based Investment and Cost Structuring Models by Miralieva Dilafruz, Ramatov Jumaniyoz, Azimova Lola, Khusamiddinova Malika, Alimbaeva Shahlo, Omonova Laylo

“…AI-enabled linguistics modeling promotes scalable optimization and context-aware applications of financial analytics, and realizes cost transparency improvements in automated investment systems. …”
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Robust Cross-Validation of Predictive Models Used in Credit Default Risk by Jose Vicente Alonso, Lorenzo Escot

“…While many methodologies have been developed, cross-validation is perhaps the most widely accepted, often being part of the model development process by optimizing the hyperparameters of predictive algorithms. …”
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Adaptive sliding mode control based on maximum power point tracking for boost converter of photovoltaic system under reference voltage optimizer by Borhen Torchani, Ahmad Taher Azar, Ahmad Taher Azar, Ahmad Taher Azar, Anis Sellami, Saim Ahmed, Saim Ahmed, Ibrahim A. Hameed, Ibraheem Kasim Ibraheem, Moamin Ibrahim Jameel Al-Obaidi

“…Simulations conducted demonstrate superior performance in terms of stability, speed, and control of the converter compared to traditional MPPT algorithms. The main contributions of this article include an improvement in system robustness against irradiance variations, thanks to the integration of an adaptive algorithm and a PI controller within the SMC. …”
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Collaborative multiview time series modeling for vehicle maintenance demand prediction by Fanghua Chen, Deguang Shang, Gang Zhou, Ke Ye, Fujie Ren, Guofang Wu

“…To enhance the interaction between distinct time points and temporal dependencies, we developed a dependency-aware learning algorithm that effectively integrates and weighs the information and dependencies at each time step, thereby improving the model’s ability to capture the complex relationships among maintenance projects over time. …”
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Enhancing surface detection: A comprehensive analysis of various YOLO models by G. Deepti Raj, B. Prabadevi

“…This study presents an improved YOLOv5 detection model, exploiting the efficient channel attention (ECA) and coordinated attention (CoordAtt) mechanisms. …”
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