Machine learning-based prediction method for open-pit mining truck speed distribution in manned operation by Changyou XU, Gang CHEN, Qiuxia ZHANG, Bo WANG, Hongwang ZHANG, Hongrui LI, Weiwei QIN, Muyang LI

“…Additionally, it aids in early prediction of hazardous situations such as speeding, thereby enhancing work safety. Machine learning also supports real-time decision-making to adapt to constantly changing circumstances. …”
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Machine learning for epithelial ovarian cancer platinum resistance recurrence identification using routine clinical data by Li-Rong Yang, Mei Yang, Liu-Lin Chen, Yong-Lin Shen, Yuan He, Zong-Ting Meng, Wan-Qi Wang, Feng Li, Zhi-Jin Liu, Lin-Hui Li, Yu-Feng Wang, Xin-Lei Luo

“…Following this screening process, five machine learning algorithms were employed to develop predictive models based on the selected variables. …”
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Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence Scenario by Jingxuan Zhang, Xiaodong Xu, Kangjie Zhang, Bufang Zhang, Xiaofeng Tao, Ping Zhang

“…Machine learning (ML) is applied to achieve flexible TTI scheduling. …”
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Flexible Job Shop Dynamic Scheduling and Fault Maintenance Personnel Cooperative Scheduling Optimization Based on the ACODDQN Algorithm by Jiansha Lu, Jiarui Zhang, Jun Cao, Xuesong Xu, Yiping Shao, Zhenbo Cheng

“…The algorithm searches the solution space by using the ACO, prioritizes the solutions by combining the non-dominated sorting strategies, and achieves the adaptive optimization of scheduling decisions by utilizing the organic integration of the pheromone update mechanism and the DDQN framework. …”
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Optimization of Nitrogen Fertilization Strategies for Drip Irrigation of Cotton in Large Fields by DSSAT Combined with a Genetic Algorithm by Zhuo Yu, Weiguo Fu

“…Building upon the DSSAT-CROPGRO model’s demonstrated superiority over pure machine learning approaches in simulating nitrogen–crop interactions (calibrated with multi-year phenological datasets), we develop a genetic algorithm-embedded decision system that simultaneously optimizes nitrogen use efficiency (NUE) and economic returns. …”
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Evolving Many-Objective Job Shop Scheduling Dispatching Rules via Genetic Programming With Adaptive Search Based on the Frequency of Features by Atiya Masood, Mansoor Ebrahim, Fahad Najeeb, Syed Muhammad Daniyal

“…The experimental results indicate that feature selection using GP and adaptive search can improve the performance of the algorithm. …”
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AI-Based Prediction of Warpage in Organic Substrates by Jingyi Zhao, Meiying Su, Rui Ma

“…Utilizing this dataset, the network architectures and hyperparameters of Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGB), and Gradient Boosting Machine (GBM) algorithms were optimized, and their performance was evaluated in terms of loss convergence, learning rate adaptability, training efficiency, and robustness. …”
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Optimizing the neural network and iterated function system parameters for fractal approximation using a modified evolutionary algorithm by Sana Abdulla, K. Mahipal Reddy

“…In this study, we propose an evolutionary optimization strategy to enhance the accuracy and adaptability of RFC splines by optimizing their scaling factor and shape parameters using our novel Fractal Differential Evolution (FDE) algorithm. …”
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An integrated stacked convolutional neural network and the levy flight-based grasshopper optimization algorithm for predicting heart disease by Syed Muhammad Salman Bukhari, Muhammad Hamza Zafar, Syed Kumayl Raza Moosavi, Majad Mansoor, Filippo Sanfilippo

“…Compared to traditional classifiers, including Regression Trees, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, and standard Neural Networks, the SCNN-LFGOA consistently outperforms these methods. …”
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Optimizing Energy Efficiency in Cloud Data Centers: A Reinforcement Learning-Based Virtual Machine Placement Strategy by Abdelhadi Amahrouch, Youssef Saadi, Said El Kafhali

“…Overall, the combination of Q-learning and the Firefly algorithm enables adaptive, SLA-compliant VM placement with improved energy efficiency.…”
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Feature extraction and fault diagnosis of gearbox based on ICEEMDAN, MPE, RF and SVM by DING Xiaofeng, ZHANG Yuhua

“…To solve the challenges related to non-stationary vibration signals in gearboxes, i.e. difficult feature extraction, high redundancy of feature vectors and low fault identification rate, this paper proposed a method of feature extraction and fault diagnosis of gearboxes based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multi-scale permutation entropy (MPE), random forest (RF) feature importance ranking and support vector machine (SVM). …”
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A visual positioning method for tunnel boring machines in underground coal mines based on anchor net features by Xuhui ZHANG, Yunkai CHI, Yuyang DU, Junying JIANG, Wenjuan YANG, Youjun ZHAO, Jicheng WAN, Yanqun WANG, Chenhui TIAN

“…BackgroundPrecise positioning of tunnel boring machines (TBMs) in underground coal mines plays a fundamental role in the automated and intelligent guidance and control of fully mechanized heading face. …”
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Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning. by Zhuoran Wang, Anoop D Shah, A Rosemary Tate, Spiros Denaxas, John Shawe-Taylor, Harry Hemingway

“…We compared the performance of S3CM with the Transductive Vector Support Machine (TVSM), the original fully-supervised Set Covering Machine (SCM) and our 'Freetext Matching Algorithm' natural language processor.…”
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Radio Propagation Models Based on Machine Learning Using Geometric Parameters for a Mixed City-River Path by Allan Dos S. Braga, Hugo A. O. Da Cruz, Leslye E. C. Eras, Jasmine P. L. Araujo, Miercio C. A. Neto, Diego K. N. Silva, Gervasio P. S. Cavalcante

“…The ANN is a Multilayer Perceptron Network (MLP) that uses the Levenberg-Marquardt training algorithm and cross-validation method. The NFS is an Adaptive Neuro-Fuzzy Inference System (ANFIS) that uses the model Sugeno. …”
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River baseflow in supplying reservoirs inflows of Tehran metropolis: A machine learning modeling based on influencing factors by Bahareh Hossein-Panahi, Sara Mohandes Samani, Amir-Reza Sadeghi, Mahsa Shahi, Seiyed Mossa Hosseini, Esmaeel Parizi

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Machine Learning-Based Prediction of Resilience in Green Agricultural Supply Chains: Influencing Factors Analysis and Model Construction by Daqing Wu, Tianhao Li, Hangqi Cai, Shousong Cai

“…The research findings are as follows: (1) fsQCA identifies a total of four high-resilience pathways, verifying the core proposition of “multiple conjunctural causality” in complex adaptive system theory; (2) compared with single algorithms such as Random Forest, Decision Tree, AdaBoost, ExtraTrees, and XGBoost, the fsQCA-XGBoost prediction method proposed in this paper achieves an optimization of 66% and over 150% in recall rate and positive sample identification, respectively. …”
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A Genetic algorithm aided hyper parameter optimization based ensemble model for respiratory disease prediction with Explainable AI. by Balraj Preet Kaur, Harpreet Singh, Rahul Hans, Sanjeev Kumar Sharma, Chetna Sharma, Md Mehedi Hassan

“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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DA-FIS: A high-speed dynamic adaptive fault injection server framework for reliable FPGA-based embedded systems by Fatimah Alhayan, Gaganjot Kaur, Sultan Alanazi, Mohammed Burhanur Rehman, Wahida Mansouri, Da’ad Albalawneh, Ali Alqazzaz, Hanadi Alkhudhayr

“…DA-FIS is implemented on the Xilinx Zynq-7000 FPGA and evaluated across multiple benchmark workloads, including the Bubble Sort algorithm, 4-bit adder, 4-bit multiplier, and counter-based logic circuits. …”
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Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models by Junchen Pan, Zhen Yue, Jing Ji, Yongping You, Liqing Bi, Yun Liu, Xinglin Xiong, Genying Gu, Ming Chen, Shen Zhang

“…The primary outcome was nosocomial pneumonia infection during ICU stay. Machine learning models including XGBoost, DecisionTree, Random Forest, Light GBM, Adaptive Boost, BP, and TabNet were used for model derivation. …”
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Residential Building Renovation Considering Energy, Carbon Emissions, and Cost: An Approach Integrating Machine Learning and Evolutionary Generation by Rudai Shan, Wanyu Lai, Huan Tang, Xiangyu Leng, Wei Gu

“…This study proposes an integrated artificial intelligence framework to facilitate multi-criteria energy renovation decision making by combining a surrogate-based machine learning (ML) model and an evolutionary generative algorithm to efficiently and accurately identify optimal renovation strategies. …”
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