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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Lightweight PUF-based anonymous authentication protocol in V2G by FAN Xinyue, LIU Jie, HE Jiahui

“…To address the issues of incomplete functionality, high communication overhead, and high computational cost in existing vehicle-to-grid (V2G) network authentication protocols, a lightweight anonymous authentication protocol based on physical unclonable function (PUF) was proposed, which could resist machine learning modeling attacks. The proposed protocol employed Hash functions and the ASCON cryptographic algorithm to achieve rapid three-party authentication and key negotiation among vehicles, charging stations, and energy providers. …”
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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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Retrospective analysis of amantadine response and predictive factors in intensive care unit patients with non-traumatic disorders of consciousness by Corinna Blum, Constanze Single, Kornelia Laichinger, Anna Hofmann, Tim W. Rattay, Tim W. Rattay, Kamaldeen Adeyemi, Reimer Riessen, Michael Haap, Helene Häberle, Ulf Ziemann, Annerose Mengel, Katharina Feil

“…Good outcome was defined as a modified Rankin Scale (mRS) of 0–2. Machine learning techniques were used to predict response to treatment.ResultsOf 442 patients (mean age 73.2 ± 10.7 years, 41.0% female), 267 (60.4%) were responders. …”
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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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Leveraging Cloud Technology for Personalized Multiple Sclerosis Care: A Comprehensive Data Management and Visualization Approach by Jose Manuel PINILLOS RUBIO, Minerva VIGUERA MORENO

“…Utilizing REDCap for data collection, we incorporated clinical outcomes and PROMs from 300 MS patients over 18 months, supporting a machine learning (ML) based clinical decision support system. …”
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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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Empowering People with Disabilities in Smart Homes Using Predictive Informing by Marko Periša, Petra Teskera, Ivan Cvitić, Ivan Grgurević

“…This paper focuses on the concept of predictive information for a person with disabilities in a smart home environment concept where artificial intelligence (AI) and machine learning (ML) systems use data on the user’s preferences, habits, and possible incident situations. …”
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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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