A Data-Driven Method for Supporting Self-Adapt Large-Scale Group Decision-Making: A Case Study on Resilient Design of Firm’s Product by Houxue Xia, Mingwei Liu, Jingyao Jiao, Huagang Tong, Haifeng Zhang

“…Large-scale group decision-making (LSGDM) has emerged as a prominent research area in various domains, such as high technology and complex engineering problems. The advent of machine learning techniques has revolutionized LSGDM by introducing new data-driven approaches. …”
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A Deep Neural Network to Identify Vacuum Degrees in Vacuum Interrupter Based on Partial Discharge Diagnosis by Hong Nhung-Nguyen, Young-Woo Youn, Yong-Hwa Kim

“…The classification performance of the proposed method is significantly better than those of machine learning algorithms such as support vector machines and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbor algorithm and the proposed method achieves an 100&#x0025; classification accuracy.…”
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Simulation of global sea surface temperature maps using Pix2Pix GAN by Deepayan Chakraborty, Adway Mitra

“…As an alternative approach, researchers are exploring if such simulated data can be generated by Generative Machine Learning models. In this work, we develop a model based on Pix2Pix conditional Generative Adversarial Network (cGAN), which can generate high-resolution spatial maps of global sea surface temperature (SST) using comparatively less computing power and time. …”
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Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests by Franck Nguyen, Selim M. Barhli, Daniel Pino Muñoz, David Ryckelynck

“…We propose a hybrid approach that simultaneously exploits a data-driven model and a physics-based model, in mechanics of materials. During a machine learning stage, a classification of possible reduced order models is obtained through a clustering of loading environments by using simulation data. …”
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Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models by Hajar Fatorachian, Kulwant Pawar

“…This study evaluates the application of machine learning techniques—ARIMA (Auto-Regressive Integrated Moving Average) and Multiple Linear Regression (MLR)—to forecast demand trends and analyze key drivers in a mid-sized cold chain operation. …”
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Image Augmentation Using Both Background Extraction and the SAHI Approach in the Context of Vision-Based Insect Localization and Counting by Ioannis Saradopoulos, Ilyas Potamitis, Iraklis Rigakis, Antonios Konstantaras, Ioannis S. Barbounakis

“…Traditional insect monitoring methods are limited in scope, but advancements in AI and machine learning enable automated, non-invasive monitoring with camera traps. …”
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Combination Hand-Crafted Features and Semi-Supervised Features Selection From Deep Features for Atrial Fibrillation Detection by Sara Mihandoost

“…The researchers aimed to utilize a machine learning approach for detecting AF from short ECG signals. …”
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Zero-Shot Traffic Identification with Attribute and Graph-Based Representations for Edge Computing by Zikui Lu, Zixi Chang, Mingshu He, Luona Song

“…Methods based on machine learning and deep learning have achieved remarkable results, but they heavily rely on the distribution of training data, which makes them ineffective in handling unseen samples. …”
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Performance Comparison of IoT Classification Models using Ensemble Stacking and Feature Importance by nabila putri setiawan, Adhitya Nugraha, Ardytha Luthfiarta, Yudha Mulyana

“…To address the imbalance in the dataset, a random undersampling technique is applied to ensure the machine learning model is not biased towards the majority class. …”
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How digital transformation can influence workflows, teaching practices and curricula in (bio)process science and engineering—An interview series with stakeholders by J. F. Buyel

“…Abstract A massive digital transformation is underway in biotechnology and process engineering fueled by recent advances in machine learning and so‐called artificial intelligence, especially in the large language model field (e.g., ChatGPT). …”
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Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents by Nicholas J. Williams, Lara Kabalan, Ljiljana Stojanovic, Viktor Zólyomi, Edward O. Pyzer-Knapp

“…Abstract A significant challenge in computational chemistry is developing approximations that accelerate ab initio methods while preserving accuracy. Machine learning interatomic potentials (MLIPs) have emerged as a promising solution for constructing atomistic potentials that can be transferred across different molecular and crystalline systems. …”
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Data Clustering for Sentiment Classification with Naïve Bayes and Support Vector Machine by Bayu Yanuargi, Ema Utami, Kusrini, Arli Aditya Parikesit

“…Clustering helps improve sentiment classification, making it more targeted and allowing a comparison of two machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM). …”
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Toward Enhanced Prediction of High‐Impact Solar Energetic Particle Events Using Multimodal Time Series Data Fusion Models by Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi

“…Our research utilizes six machine learning (ML) models, each finely tuned for time series analysis, including Univariate Time Series (UTS), Image‐based model (Image), Univariate Feature Concatenation (UFC), Univariate Deep Concatenation (UDC), Univariate Deep Merge (UDM), and Univariate Score Concatenation (USC). …”
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Multi-objective design of multi-material truss lattices utilizing graph neural networks by Ramón Frey, Michael R. Tucker, Mohamadreza Afrasiabi, Markus Bambach

“…Beyond geometric flexibility, multi-material AM further expands design possibilities by combining materials with distinct characteristics. While machine learning has recently shown great potential for the fast inverse design of lattice structures, its application has largely been limited to single-material systems. …”
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Application of big data technology in enterprise information security management by Ping Li, Limin Zhang

“…A big data analysis system that can monitor and intelligently identify potential security risks in real-time is constructed by designing complex network analysis algorithms and machine learning models. For different types of security threats, the system uses feature engineering and model training processes to extract key risk indicators and optimize model prediction performance. …”
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Deep learning–based resource allocation for secure transmission in a non-orthogonal multiple access network by Miao Zhang, Yao Zhang, Qian Cen, Shixun Wu

“…Machine learning techniques, especially deep learning algorithms have been widely utilized to deal with different kinds of research problems in wireless communications. …”
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Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self-Tuning Least Squares Support Vector Machine by Doddy Prayogo, Yudas Tadeus Teddy Susanto

“…The prediction accuracy of the ST-LSSVM was then compared to other machine learning methods, namely, LS-SVM and BPNN, and was benchmarked with the previous results by neural network (NN) from Goh using coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). …”
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A Vortex Identification Method Based on Extreme Learning Machine by Jun Wang, Lei Guo, Yueqing Wang, Liang Deng, Fang Wang, Tong Li

“…Global vortex identification methods are of high computational complexity and time-consuming. Machine learning methods are related to the size and shape of the flow field, which are weak in versatility and scalability. …”
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Areas simultaneously susceptible and (dis-)connected to debris flows in the Dolomites (Italy): regional-scale application of a novel data-driven approach by Felix Pitscheider, Stefan Steger, Marco Cavalli, Francesco Comiti, Vittoria Scorpio

“…The approach comprised the modeling of debris flow release susceptibility using an interpretable machine learning algorithm, the training of a logistic regression model, and the combination of the resultant classified maps to create a joint susceptibility-connectivity map. …”
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Load Balancing Selection Method and Simulation in Network Communication Based on AHP-DS Heterogeneous Network Selection Algorithm by Weiwei Xiao

“…This article proposes an Analytic Hierarchy Process Dempster-Shafer (AHP-DS) and similarity-based network selection algorithm for the scenario of dynamic changes in user requirements and network environment; combines machine learning with network selection and proposes a decision tree-based network selection algorithm; combines multiattribute decision-making and genetic algorithm to propose a weighted Gray Relation Analysis (GRA) and genetic algorithm-based network access decision algorithm. …”
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