Prediction Analysis of Pre-Camber for Continuous Girder Bridge Cantilever Casting Construction Based on DBO-CNN-BiLSTM-Attention Neural Network by Jinyang Zhang, Haiqing Liu, Xiangen Gong, Ming Lei, Zimu Chen

“…The research results indicate that compared to several other prediction models, the model proposed in this paper demonstrates superior performance in predicting the pre-camber of continuous girder bridges. …”
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An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets by Guangyu Mu, Jiaxue Li, Xiurong Li, Chuanzhi Chen, Xiaoqing Ju, Jiaxiu Dai

“…Existing sentiment analysis models have some limitations of applicability. Therefore, this research proposes an IDBO-CNN-BiLSTM model combining the swarm intelligence optimization algorithm and deep learning methods. …”
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The effect of training data size on real-time respiration prediction using long short-term memory model by Wenzheng Sun, Jun Dang, Lei Zhang, Qichun Wei, Chao Li, Ye Liu, Huang Jing, Kanghua Huang, Yuanpeng Zhang, Bing Li

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Integrating Data Mining, Deep Learning, and Gene Ontology Analysis for Gene Expression-Based Disease Diagnosis Systems by Sergii Babichev, Igor Liakh, Jiri Skvor

“…Bayesian optimization method was employed to determine the optimal hyperparameters for all models. The analysis of simulation results demonstrates the high efficacy of the proposed approach. …”
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PSO-optimised autoencoder for fault prediction in wind turbine planet carrier bearing by Samuel M. Gbashi, Obafemi O. Olatunji, Paul A. Adedeji, Nkosinathi Madushele

“…The study results identified the autoencoder model's optimal hyperparameters as a latent space dimension of six (6) and a leaky ReLU activation function for the hidden layer. …”
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Real-Time Explainable Multiclass Object Detection for Quality Assessment in 2-Dimensional Radiography Images by Sadra Naddaf-Sh, M-Mahdi Naddaf-Sh, Hassan Zargarzadeh, Maxim Dalton, Soodabeh Ramezani, Gabriel Elpers, Vinay S. Baburao, Amir R. Kashani

“…The models are evaluated and compared against each other, various critical hyperparameters and components are optimized, and local explainability of models is discussed. …”
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Air quality prediction using stacked bi- long short-term memory and convolutional neural network in India by S Karkuzhali, Thendral Puyalnithi, R Nirmalan

“…During the training phase, the Adam optimizer is used to fine-tune the model’s hyperparameters, with Mean Squared Error (MSE) serving as the loss function. …”
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A Study on Text Classification in the Age of Large Language Models by Paul Trust, Rosane Minghim

“…While these methods have mainly improved text generation, their implications for the text classification task are not thoroughly studied. Our research intends to bridge this gap by investigating how variations like model size, pre-training objectives, quantization, low-rank adaptation, prompting, and various hyperparameters influence text classification tasks. …”
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Physiological Sensor Modality Sensitivity Test for Pain Intensity Classification in Quantitative Sensory Testing by Wenchao Zhu, Yingzi Lin

“…These findings advance the design of sensor configurations for personalized pain management. Future research will focus on refining sensor integration and addressing stimulus-specific physiological responses.…”
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KITE-DDI: A Knowledge Graph Integrated Transformer Model for Accurately Predicting Drug-Drug Interaction Events From Drug SMILES and Biomedical Knowledge Graph by Azwad Tamir, Jiann-Shiun Yuan

“…The model does not depend on heuristic models for generating embeddings and has a minimal number of hyperparameters, making it easy to use while demonstrating outstanding performance in low-data scenarios.…”
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Surrogate modeling of passive microwave circuits using recurrent neural networks and domain confinement by Kaustab C. Sahu, Slawomir Koziel, Anna Pietrenko-Dabrowska

“…However, building accurate surrogates is a daunting task beyond simple cases (low dimensionality, narrow geometry parameter and frequency ranges). This research suggests a new technique for dependable modeling of microwave circuits. …”
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Incorporating echo state network and sand cat swarm optimization algorithm based on quantum for named entity recognition by Fang Huang, Baocheng Wang, Jafar Safarzadeh

“…The main contribution of this study is the combination of QSCSO with ESN, which improves the model’s capacity to comprehend long-term dependencies and effectively optimize hyperparameters. This research pushes forward the domain of NER and offers a scalable and efficacious architecture for related sequence labeling tasks. …”
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Enhancing Undrained Shear Strength Prediction through Innovative Hybridization Techniques by Chisom Samuel, Damilare Adewunmi

“…As a result, DTAO obtained a more suitable performance compared to other models, with R2 and RMSE equal to 0.994 and 76.142, respectively. The research outcomes have the potential to provide substantial advantages to geotechnical engineers and researchers. …”
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A Novel Multi-Fidelity Support Vector Classification Method for Boundary Prediction in Engineering Applications by Jinliang Luo, Lingzhi Liu, Youwei He, Kuan Tan

“…In response to this challenge, our research proposes an innovative multi-fidelity support vector classification approach that leverages an abundant supply of low-fidelity data alongside a limited amount of high-fidelity data. …”
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Sparse Temporal Data-Driven SSA-CNN-LSTM-Based Fault Prediction of Electromechanical Equipment in Rail Transit Stations by Jing Xiong, Youchao Sun, Junzhou Sun, Yongbing Wan, Gang Yu

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Forecasting formation density from well logging data based on machine learning model by Xiankang Cheng, Haoyu Zhang, Haoyu Zhang

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An interpretable machine learning model for predicting mortality risk in adult ICU patients with acute respiratory distress syndrome by Wanyi Li, Hangyu Zhou, Yingxue Zou

“…The aim of this study was to develop an interpretable machine learning predictive model to predict the risk of death in patients with ARDS in the ICU.MethodsThe datasets used in this study were obtained from two independent databases: Medical Information Mart for Intensive Care (MIMIC) IV and eICU Collaborative Research Database (eICU-CRD). This study used eight machine learning algorithms to construct predictive models. …”
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Recognition of multi-symptomatic rice leaf blast in dual scenarios by using convolutional neural networks by Huiru Zhou, Dingzhou Cai, Lijie Lin, Dong Huang, Bo-Ming Wu

“…Then six state-of-the-art convolutional neural network models were trained with the dataset by transfer learning and the hyperparameters of the outperforming models were further optimized to improve the recognition accuracy of models. …”
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A Data-Driven Intelligent Methodology for Developing Explainable Diagnostic Model for Febrile Diseases by Constance Amannah, Kingsley Friday Attai, Faith-Michael Uzoka

“…However, limitations such as dataset imbalance and exclusion of pediatric data remain. Future research will focus on refining the model, expanding the dataset, and conducting extensive clinical validation before real-world implementation. …”
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HSoMLSDP: A Hybrid Swarm-Optimized Machine Learning Framework for Software Defect Prediction by Madhusmita Das, Biju R. Mohan, Ram Mohana Reddy Guddeti

“…This research aims to design a hybrid swarm-optimized machine learning software defect prediction (HSoMLSDP) framework to predict software defects. …”
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