Random forest regressor for predicting sensory texture of emotional designed packaging films by Yong Ju Lee, Min Jung Joo, Ha Kyoung Yu, Tai-Ju Lee, Hyoung Jin Kim

“…This study demonstrates a robust framework for integrating machine learning in packaging design to optimize sensory appeal.…”
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Research on bearing fault diagnosis based on ISA-VMD and IMSE by Feng Yan

“…Finally, this extracted fault feature vector set is input into a multi-kernel extreme learning machine model for fault classification and diagnosis. …”
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Enhancing SMOTE for imbalanced data with abnormal minority instances by Surani Matharaarachchi, Mike Domaratzki, Saman Muthukumarana

“…This imbalance often results in sub-optimal model performance, as classifiers tend to favour the majority class. …”
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Federated Learning for Surface Roughness by Kai-Lun Cheng, Yu-Hung Ting, Wen-Ren Jong, Shia-Chung Chen, Zhe-Wei Zhou

“…This study proposes a federated learning-based real-time surface roughness prediction framework for WEDM to address issues of empirical parameter tuning and data privacy. By sharing only the model parameters, cross-machine training was enabled without exposing raw data. …”
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A discrete element simulation study of paver screed and hot mix asphalt interaction by Leandro Harries, Stefan Böhm, Jia Liu

“…This study addresses these challenges by employing a full-scale simulation using a calibrated Discrete Element Method (DEM) model to analyze HMA’s flow and pre-compaction behavior. …”
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Into the latent space of capacitive sensors: interpolation and synthetic data generation using variational autoencoders by Miguel Monteagudo Honrubia, Francisco Javier Herraiz-Martínez, Javier Matanza Domingo

“…For many sensing applications, collecting a large experimental dataset could be a time-consuming and expensive task that can also hinder the implementation of Machine Learning models for analyzing sensor data. …”
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Role of Artificial Intelligence in the Diagnosis and Management of Pulmonary Embolism: A Comprehensive Review by Ahmad Moayad Naser, Rhea Vyas, Ahmed Ashraf Morgan, Abdul Mukhtadir Kalaiger, Amrin Kharawala, Sanjana Nagraj, Raksheeth Agarwal, Maisha Maliha, Shaunak Mangeshkar, Nikita Singh, Vikyath Satish, Sheetal Mathai, Leonidas Palaiodimos, Robert T. Faillace

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Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework by Abbas Ali Hussein, Morteza Valizadeh, Mehdi Chehel Amirani, Sedighe Mirbolouk

“…However, the combination of EfficientNetB0 pre-training with ML Support Vector Machines (SVM) produced optimal results with accuracy, sensitivity, specificity, precision, F1-Score, and AUC, of 99.4%, 98.7%, 99.1%, 99%, 98.8%, and 100%, respectively. …”
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Development of interpretable intelligent frameworks for estimating river water turbidity by Amin Gharehbaghi, Salim Heddam, Saeid Mehdizadeh, Sungwon Kim

“…Therefore, knowledge of water TU plays a fundamental role in optimal managing and monitoring river water quality. …”
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A large-scale prospective nested case-control study: developing a comprehensive risk prediction model for early detection of pancreatic cancer in the community-based ESPRIT-AI coho... by Chaoliang Zhong, Penghao Li, Jia Zhao, Xue Han, Beilei Wang, Gang Jin

“…Multiple machine learning algorithms were evaluated, with the Random Forest demonstrating superior performance and selected as the final model. …”
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Unsupervised Process Anomaly Detection and Identification Using the Leave-One-Variable-Out Approach by Jacob A. Farber, Ahmad Y. Al Rashdan

“…For detection using synthetic data, the LOVO model generally outperformed comparative models; while using experimental data, the comparative methods outperformed the LOVO model. …”
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A Study of Tool Wear Prediction Based on Digital Twins by LIU Minghao, MAO Xinhui, XIA Wei, YUE Caixu, LIU Xianli

“…By integrating the physical perception layer, virtual modeling layer, data interconnection layer, and intelligent service layer, a bidirectional communication mechanism between the physical machine tool and the virtual model was established, achieving full-factor mapping and dynamic optimization of the machining process. …”
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Information Extraction from Lumbar Spine MRI Radiology Reports Using GPT4: Accuracy and Benchmarking Against Research-Grade Comprehensive Scoring by Katharina Ziegeler, Virginie Kreutzinger, Michelle W. Tong, Cynthia T. Chin, Emma Bahroos, Po-Hung Wu, Noah Bonnheim, Aaron J. Fields, Jeffrey C. Lotz, Thomas M. Link, Sharmila Majumdar

“…Unsupervised UMAP and agglomerative clustering of the pathology terms’ embeddings provided insight into model comprehension for optimized prompt design. …”
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Application of Formal Concept Analysis and Clustering Algorithms to Analyze Customer Segments by I Gede Bintang Arya Budaya, I Komang Dharmendra, Evi Triandini

“…Both K-Means and GMM algorithms recommended the optimal number of clusters as the customer segment is four. …”
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Instruction and demonstration-based secure service attribute generation mechanism for textual data by LI Chenhao, WANG Na, LIU Aodi

“…The candidate service attribute extraction component of the proposed mechanism achieves an optimal average <italic>F</italic>1 score in few-shot experiments on the CoNLL-2003 dataset, surpassing the baseline model. …”
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Experimental study on mechanical properties of triaxial geogrid reinforced marine coral sand-clay mixture based on 3D printing technology by Danda Shi, Kaiwei Xu, Zhiming Chao, Zhiming Chao, Peng Cui, Peng Cui

“…Based on the experimental results, a dataset was established, while a novel machine learning model named GP-BPNN was proposed, integrating genetic algorithm (GA), particle swarm optimization (PSO), and backpropagation neural network (BPNN). …”
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A Novel Rapid Design Framework for Tooth Profile of Double-Circular-Arc Common-Tangent Flexspline in Harmonic Reducers by Xueao Liu, Jianghao Zhang, Hui Wang, Xuecong Wang, Jianzhong Ding

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Intelligent Energy Management across Smart Grids Deploying 6G IoT, AI, and Blockchain in Sustainable Smart Cities by Mithul Raaj A T, Balaji B, Sai Arun Pravin R R, Rani Chinnappa Naidu, Rajesh Kumar M, Prakash Ramachandran, Sujatha Rajkumar, Vaegae Naveen Kumar, Geetika Aggarwal, Arooj Mubashara Siddiqui

“…By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. …”
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Workload Forecasting Methods in Cloud Environments: An Overview by Samah Aziz, Manar Kashmoola

“…We explore more sophisticated approaches like algorithms for deep learning (DL)&nbsp;and machine learning (ML)&nbsp;in addition to more conventional approaches like analysis of time series and models of regression. …”
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AI-DRIVEN INNOVATIONS IN EMERGENCY AND DISASTER RESPONSE: STRATEGIES FOR EFFECTIVE PLANNING by Manu Sudhi, Aishwarya T.R, Dasharathraj K Shetty, Jayaraj Mymbilly Balakrishnan, Sultan Ahmad, Priya Pattath Sankaran

“…AI-driven simulation models enhance preparedness by analyzing various disaster scenarios, aiding in strategic decision-making. …”
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