Risk Factors Analysis of Cutaneous Adverse Drug Reactions Caused by Targeted Therapy and Immunotherapy Drugs for Oncology and Establishment of a Prediction Model by Zimin Zhang, Mingyang Zhu, Weiwei Jiang

“…Univariate logistic analysis, least absolute shrinkage and selection operator regression, and stepwise logistic regression were utilized for feature screening. Finally, nine machine‐learning models were constructed and compared, and grid search was performed to adjust the parameters. …”
Get full text

Exploring Effects of Modified Machine Learning Pipelines of Astrochemical Inventories by Hannah Toru Shay, Haley N. Scolati, Gabi Wenzel, Kin Long Kelvin Lee, Aravindh N. Marimuthu, Brett A. McGuire

“…Machine learning pipelines for astrochemical inventories have been introduced as a useful addition to the astrochemist toolbox, having first been used to model and predict column densities in the Taurus Molecular Cloud (TMC-1). …”
Get full text

A Two-Tiered Bidirectional Atrous Spatial Pyramid Pooling-Based Semantic Segmentation Model for Landslide Classification Using Remote Sensing Images by G. NaliniPriya, E. Laxmi Lydia, Reem Alshenaifi, Radhika Kavuri, Mohamad Khairi Ishak

“…At last, the pelican optimization algorithm (POA) methodology is implemented to fine-tune the parameters involved in the RNN model. …”
Get full text

Predicting future brand value: The role of machine learning monitoring by Ahmad Al Adwan, Ghaiath Altrjman, Lu’ay Al-Mu’ani

“…Data-driven strategies have become essential for brand valuation optimization in today’s rapidly evolving virtual economy, where organizations face increasing pressure to gain real-time, accurate insights to maintain a competitive edge. …”
Get full text

Utilization of Machine Learning for Predicting Corrosion Inhibition by Quinoxaline Compounds by Muhamad Fadil, Muhamad Akrom, Wise Herowati

“…This study explores the application of Machine Learning (ML) methods based on Quantitative Structure-Properties Relationship (QSPR) to develop a predictive model for the efficiency of quinoxaline compounds as corrosion inhibitors. …”
Get full text

Deepmol: an automated machine and deep learning framework for computational chemistry by João Correia, João Capela, Miguel Rocha

“…Abstract The domain of computational chemistry has experienced a significant evolution due to the introduction of Machine Learning (ML) technologies. Despite its potential to revolutionize the field, researchers are often encumbered by obstacles, such as the complexity of selecting optimal algorithms, the automation of data pre-processing steps, the necessity for adaptive feature engineering, and the assurance of model performance consistency across different datasets. …”
Get full text

Estimation of physico-chemical properties of soil using machine learning by Patience Chizoba Mba, Opegbemi Matthias Busoye, John Temitope Ajayi, Judith Nkechinyere Njoku, Cosmas Ifeanyi Nwakanma, Senorpe Asem-Hiablie, Rammohan Mallipeddi, Tusan Park, Daniel Dooyum Uyeh

“…The optimized CNN model surpassed 90 % R² and reduced MAE by more than 50 % for several properties. …”
Get full text

Forecasting outbound student mobility: A machine learning approach. by Stephanie Yang, Hsueh-Chih Chen, Wen-Ching Chen, Cheng-Hong Yang

Get full text

A Method of Word Sense Disambiguation with Restricted Boltzmann Machine by ZHANG Chun-xiang, LI Hai-rui, GAO Xue-yao

“…For polysemy phenomenon in Chinese, Restricted Boltzmann Machine (RBM) is adopted to determine the true meaning of ambiguous vocabulary where linguistic knowledge in context is used Word form, part of speech and semantic categories in four left and right lexical units adjacent to an ambiguous word are selected as disambiguation features At the same time, RBM is used to construct word sense disambiguation (WSD) model Training corpus in SemEval-2007: Task#5 and semantic annotation corpus in Harbin Institute of Technology are used to optimize parameters of RBM Test corpus in SemEval-2007: Task#5 is used to evaluate WSD model Experimental results show that compared with Bayesian word sense disambiguation classifier, disambiguation accuracy of WSD method with RBM is improved…”
Get full text

Application and prospects of machine learning for rockfalls, landslides and debris flows by Jiazhu WANG, Yongbo TIE, Yongjian BAI, Yanchao GAO, Donghui WANG, Mingzhi ZHANG

“…Future research should prioritize enhancing data quality and quantity, optimizing model interpretability, improving model reliability and generalization, and establishing real-time monitoring and warning systems for automatic identification and rapid response to geological hazards. …”
Get full text

Machine learning-driven strategies for enhanced pediatric wheezing detection by Hye Jeong Moon, Hyunmin Ji, Hyunmin Ji, Baek Seung Kim, Beom Joon Kim, Kyunghoon Kim, Kyunghoon Kim

“…To overcome these limitations, artificial intelligence models have been developed.MethodsIn this prospective study, we aimed to compare respiratory sound feature extraction methods to develop an optimal machine learning model for detecting wheezing in children. …”
Get full text

Machine learning aids in the discovery of efficient corrosion inhibitor molecules by Haiyan GONG, Lingwei MA, Dawei ZHANG

“…Finally, this paper highlights the challenges faced by generative machine learning models in the discovery of efficient corrosion inhibitor molecules. …”
Get full text

Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification by Dharani Kanta Roy, Hemanta Kumar Kalita

“…The enhancement of deep reinforcement learning is made by associating a deep autoencoder (AE) and an improved flamingo search algorithm (IFSA) to approximate the Q-function and optimal policy selection. After feature representations, a support vector machine (SVM) classifier, which discriminates the input into normal and attack instances, is employed for classification. …”
Get full text

Hybrid modeling approaches for predicting COVID-19 mortality: A comparative study across USA, France, and India by B. Uppalaiah, D. Mallikarjuna Reddy, K. Rajalakshmi, P. Vignesh, V. Govindan, Siriluk Donganont

“…This paper proposes and assesses three Gaussian Process-based hybrid models—GP-RBM (Random Boosting Machine), GP-LSTM (Long Short-Term Memory), and GP-CNN (Convolutional Neural Network)—for forecasting COVID-19 mortality cases in the USA, France, and India. …”
Get full text

Surface Ice Detection Using Hyperspectral Imaging and Machine Learning by Steve Vanlanduit, Arnaud De Vooght, Thomas De Kerf

“…SWIR), revealing that model performance is sensitive to wavelength range, with SVM performing optimally in a reduced band set and RF benefiting from the full spectral range. …”
Get full text

Calculation Methodology and Results of Optimization of Design Parameters of the Tractor Recuperative Mounted System When Aggregated with Forest Tillage Implements by Valeryi I. Posmetyev, Maksim A. Savinkov, Viktor V. Posmetyev, Andrey Yu. Manukovskii, Vadim O. Nikonov

Get full text

Prediction of mung bean production using machine learning algorithms by Azanu Mirolgn Mequanenit, Aleka Melese Ayalew, Ayodeji Olalekan Salau, Eyerusalem Alebachew Nibret, Million Meshesha

“…A total of 10273 instances were used for the experiment. Python machine learning tool was used to conduct the experiment and build an optimal model. …”
Get full text

Dropout Kolmogorov–Arnold Networks: A Novel Data-Driven Impedance Modeling Approach for Voltage-Source Converters by Moetasem Ali, Yasser Abdel-Rady I. Mohamed

Get full text

Quantum key distribution as a quantum machine learning task by Thomas Decker, Marcelin Gallezot, Sven Florian Kerstan, Alessio Paesano, Anke Ginter, Wadim Wormsbecher

“…Abstract We propose considering Quantum Key Distribution (QKD) protocols as a use case for Quantum Machine Learning (QML) algorithms. We define and investigate the QML task of optimizing eavesdropping attacks on the quantum circuit implementation of the BB84 protocol. …”
Get full text

A novel approach to wind energy modeling in the context of climate change at Zaafrana region in Egypt by Bassem Khaled Kamel, Almoataz Y. Abdelaziz, Mahmoud A. Attia, Amr Khaled Khamees

Get full text