Development of a Preliminary Screening Tool for Predicting Polycystic Ovarian Syndrome using Machine Learning and Deep Learning Models with Non Invasive Qualitative Features: A Cas... by Hanumanth Narni, Vasudeva Rao Ananthasetty, SD Jilani

“…Aim: To develop and compare the performance of Random Forest (RF) and Feedforward Neural Network (FFNN) models in predicting PCOS using abundant non invasive qualitative features. …”
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Healthcare-associated infections in cardiac surgery: epidemiological features by E. E. Sadovnikov, N. Yu. Potseluev, O. L. Barbarash, E. B. Brusina

“…The technology of neural network modeling did not reveal neural networks suitable for describing the forecast. …”
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Reinforcing long lead time drought forecasting with a novel hybrid deep learning model: a case study in Iran by Mahnoosh Moghaddasi, Mansour Moradi, Mahdi Mohammadi Ghaleni, Zaher Mundher Yaseen

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EnsembleXAI-Motor: A Lightweight Framework for Fault Classification in Electric Vehicle Drive Motors Using Feature Selection, Ensemble Learning, and Explainable AI by Md. Ehsanul Haque, Mahe Zabin, Jia Uddin

“…Finally, it contributes to the development of safer and more reliable EV systems through the development of models supervised on fewer features to give the computing time that is a little lighter without compromising its diagnostic performance.…”
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Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data by Seung-Hwan Choi, Dawn An, Inho Lee, Suwoong Lee

“…To address this issue, we employ a semi-supervised learning approach that relies solely on normal data to effectively detect abnormal patterns, overcoming the limitations of conventional methods. The performance of semi-supervised models was first validated using a statistical feature-based anomaly detection approach, from which the GCN-VAE model was adopted. …”
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Enhancing Early Breast Cancer Detection with Infrared Thermography: A Comparative Evaluation of Deep Learning and Machine Learning Models by Reem Jalloul, Chethan Hasigala Krishnappa, Victor Ikechukwu Agughasi, Ramez Alkhatib

“…This study investigates and compares the performance of various deep learning and machine learning models in analyzing thermographic data to classify breast tissue as healthy, benign, or malignant. …”
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Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network by Qingguo Wei, Chang Li, Yijun Wang, Xiaorong Gao

“…The performance of the proposed method is validated on two SSVEP BCI datasets and compared with that of eTRCA, sbCNN and other state-of-the-art models. …”
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Prediction of adverse drug reactions using demographic and non-clinical drug characteristics in FAERS data by Alireza Farnoush, Zahra Sedighi-Maman, Behnam Rasoolian, Jonathan J. Heath, Banafsheh Fallah

“…We demonstrated that our parsimonious models, which include only the top 20 most important features comprising 5 demographic features and 15 non-clinical features (13 molecular and 2 biological), achieve ADR prediction performance comparable to a less practical, feature-rich model consisting of all 2,315 features. …”
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An Enhanced Framework for Assessing Pluvial Flooding Risk with Integrated Dynamic Population Vulnerability at Urban Scale by Xinyi Shu, Chenlei Ye, Zongxue Xu, Ruting Liao, Pengyue Song, Silong Zhang

“…Additionally, using multi-source remote sensing data, dynamic population vulnerability, and flood hazard processes, a quantitative dynamic flood risk analysis is conducted based on cloud models. The results demonstrated the following: (1) PSO performed best in calibrating the SWMM in the study area, with Nash–Sutcliffe efficiency (NSE) values ranging from 0.93 to 0.69. (2) Drainage system capacity was low, with over 90% of the network exceeding capacity in scenarios with return periods of 1 to 100 years. (3) The vulnerability of people and buildings increased with higher flood intensity and duration. …”
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Federated Learning Enhanced MLP–LSTM Modeling in an Integrated Deep Learning Pipeline for Stock Market Prediction by Jayaraman Kumarappan, Elakkiya Rajasekar, Subramaniyaswamy Vairavasundaram, Ketan Kotecha, Ambarish Kulkarni

“…There are two types of models for each of the three basic elements within the Fed-MLP–LSTM, namely, MLP for feature extraction and LSTM for sequence modeling. …”
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Exploring how course social and cultural environmental features influence student engagement in STEM active learning courses: a control–value theory approach by Yoon Ha Choi, Elli Theobald, Vicente Velasco, Sarah L. Eddy

“…We used structural equation modeling to map how features of the course environment related to control, value, and academic emotions, as well as how control, value, and academic emotions influenced engagement. …”
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A river network model using a weight-based merged LSTM for multi-source monitoring integration by Jonggyu Jung, Taeseung Park, Jaegwan Park, Dogeon Lee, YoonKyung Cha

“…While graph neural networks (GNNs) have shown promise in modeling spatial connectivity, they remain limited by reliance on features common to all nodes. …”
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Construction of feature selection and efficacy prediction model for transformation therapy of locally advanced pancreatic cancer based on CT, 18F-FDG PET/CT, DNA mutation, and CA19... by Liang Qi, Xiang Li, Jiayao Ni, Yali Du, Qing Gu, Baorui Liu, Jian He, Juan Du

“…Subsequently, we separately or in combination modeled the CT features, PET features, baseline CA199, and gene mutation data to construct efficacy prediction models. …”
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Cross-Session Emotion Recognition by Joint Label-Common and Label-Specific EEG Features Exploration by Yong Peng, Honggang Liu, Junhua Li, Jun Huang, Bao-Liang Lu, Wanzeng Kong

“…Results obtained from the SEED-IV and SEED-V emotional data sets experimentally demonstrate that JCSFE not only achieves superior emotion recognition performance in comparison with the state-of-the-art models but also provides us with a quantitative method to identify the label-common and label-specific EEG features in emotion recognition.…”
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Development of a multidimensional data model for efficient content-based image retrieval in big data storage by Stanislav Danylenko, Serhii Smelyakov

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An intelligent framework for skin cancer detection and classification using fusion of Squeeze-Excitation-DenseNet with Metaheuristic-driven ensemble deep learning models by J. D. Dorathi Jayaseeli, J Briskilal, C. Fancy, V. Vaitheeshwaran, R. S. M. Lakshmi Patibandla, Khasim Syed, Anil Kumar Swain

“…Furthermore, the proposed DSC-EDLMGWO model utilizes the SE-DenseNet method, which is the fusion of the squeeze-and-excitation (SE) module and DenseNet to extract features. …”
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Assessment of the accuracy of heavy aircraft control, taking into account the functioning of the indicator on the windshield and flight control actuators by A. A. Malchenko, P. S. Kostin, Ya. G. Khatuntsev

“…The principle of integration of a Simulink model of a hydraulic system and a flash model of a windshield indicator with a model of spatial motion of a heavy aircraft is presented. …”
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An automatic classification of breast cancer using fuzzy scoring based ResNet CNN model by Nisha Y., Jagadeesh Gopal

“…So, this research introduces a hybrid DL model for improving prediction performance andreducing time consumption compared to the machine learning (ML)model.Describing a pre-processing method utilizing statistical co-relational evaluation to improve the classifier’s accuracy.The features are then extracted from the Region of Interest (ROI) images using the wrapping technique and a fast discrete wavelet transform (FDWT). …”
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Optimization of a Heavy-Duty Hydrogen-Fueled Internal Combustion Engine Injector for Optimum Performance and Emission Level by Murat Ozkara, Mehmet Zafer Gul

“…This study presents a high-fidelity optimization framework that couples a validated computational fluid dynamics (CFD) combustion model with a surrogate-assisted multi-objective genetic algorithm (MOGA). …”
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Enhancing game outcome prediction in the Chinese basketball league through a machine learning framework based on performance data by Yuhua Zhong

“…The results reveal that the incorporation of additional features substantially enhances predictive performance. …”
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