Support Vector Machine for Behavior-Based Driver Identification System by Huihuan Qian, Yongsheng Ou, Xinyu Wu, Xiaoning Meng, Yangsheng Xu

“…Then we compare fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) for data preprocessing. Using machine learning method of support vector machine (SVM), we derive the individual driving behavior model and we then demonstrate the procedure for recognizing different drivers by analyzing the corresponding models. …”
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Suggesting a Novel Hybrid Approach for Predicting Solar Irradiance in the Qinghai Province of China by Baran Yılmaz, Rachel Samra

“…This work aims to provide a hybrid model using machine learning to accurately predict solar Direct normal irradiance with the least amount of error. …”
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A short investigation of the effect of the selection of human brain atlases on the performance of ASD's classification models by Naseer Ahmed Khan, Xuequn Shang

“…Through a systematic review, we examined the performance of different atlases in various machine-learning and deep-learning frameworks for ASD classification. …”
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Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression by Mayumi Kamada, Yusuke Sakuma, Morihiro Hayashida, Tatsuya Akutsu

“…Moreover, we perform computational experiments employing two machine learning methods, support vector regression (SVR) and relevance vector machine (RVM), for dataset obtained from biological experiments. …”
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Improving Predictions of Multiple Binary Models in ILP by Tarek Abudawood

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QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules by Vivin Vinod, Peter Zaspel

“…Abstract Progress in both Machine Learning (ML) and Quantum Chemistry (QC) methods have resulted in high accuracy ML models for QC properties. …”
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Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review by Claire R. van Genugten, Claire R. van Genugten, Melissa S. Y. Thong, Melissa S. Y. Thong, Melissa S. Y. Thong, Wouter van Ballegooijen, Wouter van Ballegooijen, Wouter van Ballegooijen, Annet M. Kleiboer, Annet M. Kleiboer, Donna Spruijt-Metz, Arnout C. Smit, Mirjam A. G. Sprangers, Mirjam A. G. Sprangers, Yannik Terhorst, Yannik Terhorst, Heleen Riper, Heleen Riper, Heleen Riper

“…To accomplish this, JITAIs often apply complex analytic techniques, such as machine learning or Bayesian algorithms to real- or near-time data acquired from smartphones and other sensors. …”
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RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer’s disease by Manuel Lentzen, Srinivasan Vairavan, Marijn Muurling, Vasilis Alepopoulos, Alankar Atreya, Merce Boada, Casper de Boer, Pauline Conde, Jelena Curcic, Giovanni Frisoni, Samantha Galluzzi, Martha Therese Gjestsen, Mara Gkioka, Margarita Grammatikopoulou, Lucrezia Hausner, Chris Hinds, Ioulietta Lazarou, Alexandre de Mendonça, Spiros Nikolopoulos, Dorota Religa, Gaetano Scebba, Pieter Jelle Visser, Gayle Wittenberg, Vaibhav A. Narayan, Neva Coello, Anna-Katharine Brem, Dag Aarsland, Holger Fröhlich, on behalf of RADAR-AD

“…Additionally, we employed four machine learning classifiers – Logistic Regression, Decision Tree, Random Forest, and XGBoost – using a nested cross-validation approach to assess the discriminatory capabilities of the RMTs. …”
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Analisis Sentimen Pada Sosial Media Twitter Terhadap Kualitas Jaringan Internet Telkomsel Menggunakan Ensemble K-Nearest Neighbour -Support Vector Machine by Muchammad Farchan Fachrudin, Cucun Very Angkoso, Doni Abdul Fatah

“…Dalam penelitian analisis sentimen ini menggunakan model algoritma machine learning yaitu K-Nearest Neighbour, Support Vector Machine, dan Ensemble KNN-SVM yang berbasis majority vote dan berbasis average. …”
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Optimasi Algoritma Naive Bayes dengan Diskritisasi K-Means pada Diagnosis Penyakit Jantung by Nafa Fajriati, Budi Prasetiyo

“…Dataset yang digunakan yaitu Cleveland heart disease dataset dari UCI Machine Learning Repository. Untuk meningkatkan akurasi klasifikasi menggunakan algoritma Naive Bayes, atribut kontinu pada dataset diubah menjadi atribut diskrit dengan diskritisasi K-means. …”
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IL-23 tunes inflammatory functions of human mucosal-associated invariant T cells by Laetitia Camard, Tharshana Stephen, Hanane Yahia-Cherbal, Vincent Guillemot, Sébastien Mella, Victoire Baillet, Hélène Lopez-Maestre, Daniele Capocefalo, Laura Cantini, Claire Leloup, Julie Marsande, Katherine Garro, Juan Sienes Bailo, Ambre Dangien, Natalia Pietrosemoli, Milena Hasan, Huimeng Wang, Sidonia B.G. Eckle, Anne M. Fourie, Carrie Greving, Barbara Joyce-Shaikh, Raphaelle Parker, Daniel J. Cua, Elisabetta Bianchi, Lars Rogge

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Sistem Rekognisi Citra Digital Bahasa Isyarat Menggunakan Convolutional Neural Network dan Spatial Transformer by Mohammad Alfiano Rizky Mahardika, Novanto Yudistira, Achmad Ridok

“…Dengan menggunakan metode Convolutional Neural Network (CNN) yang merupakan bagian dari Deep Learning atau Machine Learning, sistem akan mengenali pose atau bentuk dari citra bahasa isyarat yang dimasukkan, dan memberikan luaran yang sesuai dengan maksud dari pose atau bentuk dari citra bahasa isyarat tersebut. …”
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Friend Recommender System for Social Networks Based on Stacking Technique and Evolutionary Algorithm by Aida Ghorbani, Amir Daneshvar, Ladan Riazi, Reza Radfar

“…It is challenging to choose the best machine learning approach to address the recommender system issue since there are several strategies with various benefits and drawbacks. …”
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Analysis and Prediction of the Interval Duration between the First and Second Accidents considering the Spatiotemporal Threshold by Fang Liu, Lanlan Zheng, Mingyang Li, Jinjun Tang

“…Finally, the random forest model (RF), which combines the advantages of machine learning methods, is employed to predict the duration of the interval. …”
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AI-Driven Signal Processing for SF6 Circuit Breaker Performance Optimization by Philippe A. V. D. Liz, Giovani B. Vitor, Ricardo T. Lima, Aurélio L. M. Coelho, Eben P. Silveira

“…Thus, the proposed modeling considers the signal preprocessing steps for feature extraction, the generation of the dataset for model training, the use of different machine learning techniques to automatically find the desired points, and, finally, the identification of the best moments for controlled switching of the circuit breakers. …”
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LiDAR-Based Windshear Detection via Statistical Features by Jie Zhang, Pak Wai Chan, Michael K. Ng

“…With the development of machine learning, several learning-based methods are proposed for windshear detection, i.e., windshear and non-windshear classification. …”
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A Computational Complexity-Based Method for Predicting Scholars’ Ages through Articles’ Information by Jun Zhang, Xiaoyan Su, Mingliang Hou, Jing Ren

“…Our method first classifies factors that affect scholars’ ages into intuitive and complex types according to their computational complexity and then apply machine learning algorithms to predict the ages of scholars based on these factors. …”
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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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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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