Analyzing the efficacy of trimethylolpropane trioleate oil for predicting cutting power and surface roughness in high-speed drilling of Al-6061 through machine learning. by Pramod S Kathmore, Bhanudas D Bachchhav, Duran Kaya, Sachin Salunkhe, Lenka Cepova, Ondřej Mizera, Emad Abouel Nasr

“…The decision tree performed better than other models in accurately predicting power and surface roughness. …”
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Geogenic perspectives on potassium dynamics and plant uptake: insights from natural and submerged conditions across different soil types with machine learning predictions by Saibal Ghosh, Gourav Mondal, Shreya Chakraborty, Sonali Banerjee, Sumit Kumar, Riddhi Basu, Pradip Bhattacharyya

“…Sensitivity analysis indicated that WsK and ExK from non-submerged soil to be the most favorable forms for potassium uptake, especially in the rice roots and grains. Machine learning models, particularly Random Forest, accurately predicted potassium availability and uptake, highlighting their potential in optimizing soil fertility and advancing precision agriculture for better crop yields and soil health.…”
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How does high temperature weather affect tourists' nature landscape perception and emotions? A machine learning analysis of Wuyishan City, China. by Cuicui Ye, Zhengyan Chen, Zheng Ding

“…In this study, we employed machine learning models such as LSTM-CNN, Hrnet, and XGBoost, combined with hotspot analysis and SHAP methods, to compare and reveal the potential impacts of natural landscape elements on tourists' emotions under different temperature conditions. …”
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Predicting Minimum Temperatures of Plastic Greenhouse During Strawberry Growing in Changfeng, China: A Comparison of Machine Learning Algorithms and Multiple Linear Regression by Xuelin Wang, Qinqin Huang, Dong Wu, Jinhua Xie, Ming Cao, Jun Liu

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Design and Optimization of Permanent Magnet Flux-Switching Generator Arrangement Spoke by Taguchi Method for Direct-Drive Wind Turbines by Pouria Nadri, Mohammad Reza Besmi

“…Finally, the effectiveness of the proposed optimization approach was validated by comparing it to the base model. …”
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Smart waste classification in IoT-enabled smart cities using VGG16 and Cat Swarm Optimized random forest. by Akshat Gaurav, Brij Bhooshan Gupta, Varsha Arya, Razaz Waheeb Attar, Shavi Bansal, Ahmed Alhomoud, Kwok Tai Chui

“…In this context, this work presents a waste categorization model based on transfer learning using the VGG16 model for feature extraction and a Random Forest classifier tuned by Cat Swarm Optimization (CSO). …”
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Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm by Nasiru Muhammad Dankolo Dankolo, Nor Haizan Mohamed Radzi, Noorfa Haszlinna Mustaffa, Mohd Shukor Talib, Zuriahati Mohd Yunos, Danlami Gabi

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Improved firefly algorithm–extended Kalman filter–least-square support-vector machine voltage sag monitoring and classification method based on edge computing by Zhu Liu, Xuesong Qiu, Yonggui Wang, Shuai Zhang, Zhi Li

“…Extract characteristic quantities such as average value, duration of sag, minimum sag dispersion characteristics, number of sag phases, and flow direction of disturbance energy. As a model training data set, the least-square support-vector machine method optimized based on the improved firefly algorithm is used to create a multi-level classification model of voltage sag source to realize the classification of voltage sag sources. …”
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Improved Nonlinear Dynamic Model of Helical Gears Considering Frictional Excitation and Fractal Effects in Backlash by Guoyin Mo, Chengyu Liu, Guimian Liu, Fuhao Liu

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Optimization of milling process of AISI 4340 Steel for enhanced tool life and surface quality using response surface methodology and bayesian technique by Laura Peña-Parás, Martha Rodríguez-Villalobos, Demófilo Maldonado-Cortés, Elisa Margarita Mendoza-Zamarripa, Stephany Elizabeth Vargas-Piedra, Sumaiya Saima Sultana, Octavio Muñiz-Cepeda, Héctor de la Fuente

“…Milling is a widely used machining process in the manufacturing industry employed to remove material from a workpiece. …”
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An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms by Maithri Bairy, Krishnaraj Chadaga, Niranjana Sampathila, R. Vijaya Arjunan, G. Muralidhar Bairy

“…Fourteen nature-inspired feature selection algorithms were applied to identify the most informative markers while optimizing the predictive models for greater accuracy and reliability. …”
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A Novel Dual-Channel Hybrid Attention Model for Wind Turbine Misalignment Fault Diagnosis by Tong Tong, Xiang Liu, Jia Zhang, Dian Long, Teng Fan, Xiangyang Zheng

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A hybrid approach for intrusion detection in vehicular networks using feature selection and dimensionality reduction with optimized deep learning. by Fayaz Hassan, Zafi Sherhan Syed, Aftab Ahmed Memon, Saad Said Alqahtany, Nadeem Ahmed, Mana Saleh Al Reshan, Yousef Asiri, Asadullah Shaikh

“…Results demonstrate the effectiveness of feature engineering which improves the classification f1score from 96.48% to 98.43%. It also reduces the model size from 28.09 KB to 20.34 KB thus optimizing the model in terms of both classification performance and model size. …”
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Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation by H. Huang, Q. Wang, C. Liu, C. Zhou

“…<p>While traditional thermal infrared retrieval algorithms based on radiative transfer models (RTMs) could not effectively retrieve the cloud optical thickness of thick clouds, machine-learning-based algorithms were found to be able to provide reasonable estimations for both daytime and nighttime. …”
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Internet of things driven hybrid neuro-fuzzy deep learning building energy management system for cost and schedule optimization by Deepshikha Shrivastava, Deepshikha Shrivastava, Prerna Goswami

“…The data collected was preprocessed, cleaned, transformed and used for training a machine learning model. Based on the previous literature, a hybrid DL model was developed using artificial neural networks and fuzzy logic by integrating fuzzy layers in the deep neural architecture. …”
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Long Short-Term Memory Networks and Bayesian Optimization for Predicting the Time-Weighted Average Pressure of Shield Supporting Cycles by Wanzi Yan, Junhui Wang, Jingyi Cheng, Zhijun Wan, Keke Xing, Kuidong Gao

“…In this study, a hybrid machine learning model integrating the long short-term memory (LSTM) networks and the Bayesian optimization (BO) algorithm was developed to predict TWAP based on the setting pressure (SP), revised setting pressure (RSP), final pressure (FP), number of yielding (NY), TWAP in the last supporting cycle (TWAP (last)), and loading rate in each period. …”
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Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Signal Phase and Timing Information at Actuated Traffic Signals by Seifeldeen Eteifa, Amr Shafik, Hoda Eldardiry, Hesham A. Rakha

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Co-Optimization of Speed Planning and Energy Management for Plug-In Hybrid Electric Trucks Passing Through Traffic Light Intersections by Xin Liu, Guojing Shi, Changbo Yang, Enyong Xu, Yanmei Meng

“…The model’s validity is confirmed through testing on a hardware-in-the-loop test machine, followed by simulation experiments. …”
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Support Vector Machine Berbasis Feature Selection Untuk Sentiment Analysis Kepuasan Pelanggan Terhadap Pelayanan Warung dan Restoran Kuliner Kota Tegal by Oman Somantri, Dyah Apriliani

“…Sentiment analysis is used to provide a solution related to this problem by applying the Support Vector Machine (SVM) algorithm model. The purpose of this research is to optimize the generated model by applying feature selection using Informatioan Gain (IG) and Chi Square algorithm on the best model produced by SVM on the classification of customer satisfaction level based on culinary restaurants at Tegal City so that there is an increasing accuracy from the model. …”
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Meta-RHDC: Meta Reinforcement Learning Driven Hybrid Lyrebird Falcon Optimization for Dynamic Load Balancing in Cloud Computing by Mallu Shiva Rama Krishna, D. Khasim Vali

“…The Meta-RHDC model leverages convolutional and recurrent neural networks to predict virtual machine loads and dynamically classify them into overloaded and underloaded categories. …”
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