... Movement patterns Weight-matrix factorization Machine learning National Science and Technology Council, Taiwan NSTC 111-2410-H-033-013 http://dx.doi.org/10.13039/501100020950 National Science and Technology Council, Taiwan NSTC 113-2410-H-033-025 http://dx.doi.org/10.13039...

... Limited 2025 Emerald Publishing Limited Licensed re-use rights only Data science Machine learning Class imbalance learning Over-sampling Ensemble learning Chang Gung Memorial Hospital, Linkou BMRPH13 http://dx.doi.org/10.13039/501100005795 Institute for Information Industry...

... learning methods and determined the best-performing model for identifying potential high-quality patents. Findings Among the various machine learning models with 4 evaluation metrics (accuracy, recall, precision and F1), when introduced to a single feature (CDF or CSF), Random Forest is the best model...

.... Most significantly, we develop a machine learning fusion model that combines Gradient Boosting Decision Trees (GBDT) with Multilayer Perceptron (MLP), leveraging deep learning and big data technologies. Findings This model not only improves the accuracy of identifying opinion leaders, but also...

...Jiho Kim; Hongchul Lee; Hanjun Lee Purpose In this study, we propose a model to forecast the helpfulness of online company reviews and understand the influence of identified topics on this perceived helpfulness. Design/methodology/approach Our approach involves constructing machine learning...

... of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media...

... responses that reflect the customer’s unique personality. Our approach is twofold: firstly, we employ the customer complaints ontology (CCOntology) framework enforced with multi-class classification based on a machine learning algorithm, to classify complaints. Secondly, we leverage the personality...

... 2024 20 04 2024 24 05 2024 20 06 2024 © Emerald Publishing Limited 2024 Emerald Publishing Limited Licensed re-use rights only Educational data mining Learning difference Deep learning Deep neural network Frequent patterns mining Machine learning Educational...

...Chia Yu Hung; Eddie Jeng; Li Chen Cheng Purpose This study explores the career trajectories of Chief Executive Officers (CEOs) to uncover unique characteristics that contribute to their success. By utilizing web scraping and machine learning techniques, over two thousand CEO profiles from LinkedIn...

...Zicheng Zhang Purpose Advanced big data analysis and machine learning methods are concurrently used to unleash the value of the data generated by government hotline and help devise intelligent applications including automated process management, standard construction and more accurate dispatched...

.... The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA). Design/methodology/approach This work uses the collected FMEA historical data to predict the probability of component/product failure...

.... Machine learning models can be employed to predict driver genes implicated in causative mutations. Design/methodology/approach In the present study, a comprehensive next generation sequencing (NGS) analysis of whole exome sequences of 47 OC patients was carried out to identify clinically significant...

... apply machine learning methods for results merging in federated patent search. Even though several methods for results merging have been developed, none of them were tested on patent data nor considered several machine learning models. Thus, the authors experiment with state-of-the-art methods using...

.../methodology/approach Related tweets were retrieved using relevant hashtags followed by multiple data cleaning procedures. Foundational machine learning methods such as Naive Bayes, Support Vector Machine, Logistic Regression (LR) and Decision Tree were implemented. For each classifier, two feature extraction...

...Vahide Bulut Purpose Surface curvature is needed to analyze the range data of real objects and is widely applied in object recognition and segmentation, robotics, and computer vision. Therefore, it is not easy to estimate the curvature of the scanned data. In recent years, machine learning...

.../approach In this paper, global vectors (GloVe) and Bidirectional Encoder Representations Transformers (BERT) pre-trained word embedding are used to identify relationships between words, which helps to classify emails into their relevant categories using machine learning and deep learning models. Two...

... and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification...

...Zhenyuan Wang; Chih-Fong Tsai; Wei-Chao Lin Purpose Class imbalance learning, which exists in many domain problem datasets, is an important research topic in data mining and machine learning. One-class classification techniques, which aim to identify anomalies as the minority class from the normal...

... behavior: (1) using several machine learning classifiers based on sentimental dimensions of customers' reviews and (2) conducting the experiment consisted of two subsections. In the experiment, the first subsection is designed for participants to predict whether customers who wrote reviews would visit...

... for indifference, preference and veto thresholds, and the study’s algorithm can find the criteria weights, reference profiles and the lambda cutting level. The study’s approach is inspired by a machine learning ensemble technique, the random forest, and for that, the authors named the study’s approach as ELECTRE...