Attention mechanism based CNN-LSTM hybrid deep learning model for atmospheric ozone concentration prediction by Jiang Yuan, Hua Dengxin, Wang Yufeng, Yang Xueting, Di Huige, Yan Qing

“…It features an attention-based CNN-LSTM hybrid deep learning model with specific settings: a time step of 5, a batch size of 25, 15 units in the LSTM layer, the ReLU activation function, 25 epochs, and an overfitting avoidance strategy with a dropout rate of 0.15. …”
Get full text

Design of an Efficient Model for Psychological Disease Analysis and Prediction Using Machine Learning and Genomic Data Samples by Alparthi Kumuda, Saroj Kumar Panigrahy

“…Therefore, this study developed the Psychological Disorders Machine Learning Genomic (PDMLG) model as an amalgamation of genetic algorithms and machine learning techniques in a predictive analysis model using genomic data samples. …”
Get full text

Hybrid Machine Learning Model for Hurricane Power Outage Estimation from Satellite Night Light Data by Laiyin Zhu, Steven M. Quiring

“…We found that the two-step hybrid model significantly improved model prediction performance by capturing a substantial portion of the uncertainty in the zero-inflated data. …”
Get full text

Toward the Content Language Integrated Learning (CLIL) Shift: What are EFL Teachers’ Understandings, Practices, and Needs? by Trinh Quoc Lap, Phan Ngoc Tuong Vy, Nguyen Thi My Hanh, Le Cong Tuan, Nguyen Thanh Hung

“…Reflecting this trend, Vietnam’s educational reforms under the 2018 General Education Program have incorporated CLIL-informed lessons into newly adopted English textbooks, signaling a step toward integrating language and subject learning. …”
Get full text

Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach by Sumaira Tabassum, M. Jawad Khan, Javaid Iqbal, Asim Waris, M. Adeel Ijaz

“…It is fine-tuned on labeled data, followed by a classification step using a Convolutional Neural Network (CNN). …”
Get full text

Automated identification of autism spectrum disorder from facial images using explainable deep learning models by El-Sayed Atlam, Khulood O. Aljuhani, Ibrahim Gad, Elsaid Md. Abdelrahim, Ahmed E. Mansour Atwa, Ali Ahmed

“…However, existing machine learning and deep learning techniques frequently face challenges such as limited generalizability, inadequate interpretability, and insufficient performance on diverse datasets. …”
Get full text

Deep Learning-Enhanced Motor Training: A Hybrid VR and Exoskeleton System for Cognitive–Motor Rehabilitation by Kathya P. Acuña Luna, Edgar Rafael Hernandez-Rios, Victor Valencia, Carlos Trenado, Christian Peñaloza

“…Key innovations included a motor imagery EEG acquisition protocol for data classification and a machine learning framework leveraging deep learning with a wavelet packet transform for feature extraction. …”
Get full text

A Novel Improvement of Feature Selection for Dynamic Hand Gesture Identification Based on Double Machine Learning by Keyue Yan, Chi-Fai Lam, Simon Fong, João Alexandre Lobo Marques, Richard Charles Millham, Sabah Mohammed

“…In contrast, causal machine learning goes a step further by revealing causal relationships between different variables. …”
Get full text

Snow Depth Extraction From Time‐Lapse Imagery Using a Keypoint Deep Learning Model by C. M. Breen, W. R. Currier, C. Vuyovich, Z. Miao, L. R. Prugh

Get full text

Detection and classification of hypertensive retinopathy based on retinal image analysis using a deep learning approach by Bambang Krismono Triwijoyo, Ahmat Adil, Muhammad Zulfikri

“…The dataset is divided into 60 % training and 40 % validation data. The next step is the image analysis process, which involves extracting retinal blood vessels using the Otsu segmentation algorithm. …”
Get full text

Balancing Predictive Performance and Interpretability in Machine Learning: A Scoring System and an Empirical Study in Traffic Prediction by Fabian Obster, Monica I. Ciolacu, Andreas Humpe

“…This comprehensive methodology includes stratified sampling, model tuning, and a two-step ranking system to operationalize this trade-off. …”
Get full text

Context-Aware Machine Learning-Based Beam Selection With Multi-Panel Devices in the Presence of Self-Blockage by Sajad Rezaie, Joao Morais, Ahmed Alkhateeb, Preben Mogensen, Carles Navarro Manchon

“…Context-aware beam management in millimeter-wave (mmWave) wireless communication systems has received increasing attention over the past few years. Machine learning (ML) has played a key role in leveraging different types of context information from the device position and orientation to more ambitious scenarios using RADAR, LIDAR, or camera images. …”
Get full text

ScarpLearn: an automatic scarp height measurement of normal fault scarps using convolutional neural networks by Léa Pousse-Beltran, Theo Lallemand, Laurence Audin, Pierre Lacan, Andres David Nunez-Meneses, Sophie Giffard-Roisin

“…We compared the results obtained with ScarpLearn and other non deep-learning methods. ScarpLearn achieves similar accuracy while being much faster and having smaller uncertainties. …”
Get full text

Facilitating automated fact-checking: a machine learning based weighted ensemble technique for claim detection by Md. Rashadur Rahman, Rezaul Karim, Mohammad Shamsul Arefin, Pranab Kumar Dhar, Gahangir Hossain, Tetsuya Shimamura

“…This paper proposes a novel ensemble machine learning framework for the effective detection of claims in a low-resource language like Bangla, a critical initial step in the automated fact-checking process. …”
Get full text

Investigating the Capabilities of Ensemble Machine Learning Model in Identifying Near-Fault Pulse-Like Ground Motions by Jafar Al Thawabteh, Jamal Al Adwan, Yazan Alzubi, Ahmad Al-Elwan

“…These motions, characterized by their unique directivity or fling step effects, pose a substantial challenge in earthquake engineering. …”
Get full text

MobDenseNet: A hybrid deep learning model for brain tumor classification using MRI by Meher Afroj, M. Rubaiyat Hossain Mondal, Md Riad Hassan, Sworna Akter

“…The proposed MobDenseNet is developed using the concepts of existing deep learning models: MobileNetV1 and DenseNet; the model incorporates hyperparameter fine-tuning and feature fusion ensemble during the feature extraction phase, consolidating layers like batch normalization, dense layers in the classification step to classify brain tumors. …”
Get full text

An Online‐Learned Neural Network Chemical Solver for Stable Long‐Term Global Simulations of Atmospheric Chemistry by Makoto M. Kelp, Daniel J. Jacob, Haipeng Lin, Melissa P. Sulprizio

“…Abstract A major computational barrier in global modeling of atmospheric chemistry is the numerical integration of the coupled kinetic equations describing the chemical mechanism. Machine‐learned (ML) solvers can offer order of magnitude speedup relative to conventional implicit solvers but past implementations have suffered from fast error growth and only run for short simulation times (<1 month). …”
Get full text

High-throughput screening and machine learning classification of van der Waals dielectrics for 2D nanoelectronics by Yuhui Li, Guolin Wan, Yongqian Zhu, Jingyu Yang, Yan-Fang Zhang, Jinbo Pan, Shixuan Du

“…Moreover, we developed a high-accuracy two-step machine learning (ML) classifier for screening dielectrics. …”
Get full text

Multiscale computational framework linking alloy composition to microstructure evolution via machine learning and nanoscale analysis by Jaemin Wang, Hyeonseok Kwon, Sang-Ho Oh, Jae Heung Lee, Dae Won Yun, Hyungsoo Lee, Seong-Moon Seo, Young-Soo Yoo, Hi Won Jeong, Hyoung Seop Kim, Byeong-Joo Lee

“…An advanced screening step incorporated nanoscale physical descriptors that capture mechanisms governing precipitate coarsening and dynamic recrystallization. …”
Get full text

Translational analysis of data science and causal learning in real-world clinical evaluation of traditional Chinese medicine by Wei Yang, Danhui Yi, XiaoHua Zhou, Yuanming Leng

“…The methodology involves several key steps, including data integration and warehouse building, high-dimensional feature selection, the use of interpretable statistical machine learning algorithms, complex networks, and graph network analysis, knowledge mining techniques such as natural language processing and machine learning, observational study design, and the application of artificial intelligence tools to build an intelligent engine for translational analysis. …”
Get full text