Neural network-based evaluation method for alignment state of air spring vibration isolation device by Zhiwei LIU, Liang SHI, Song LIU

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Improving the Universal Performance of Land Cover Semantic Segmentation Through Training Data Refinement and Multi-Dataset Fusion via Redundant Models by Jae Young Chang, Kwan-Young Oh, Kwang-Jae Lee

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Development of approach to an automated acquisition of static street view images using transformer architecture for analysis of Building characteristics by Seunghyeon Wang

“…However, differences in performance among most models were not statistically significant. Finally, this research discusses the practical implications and applications of these findings in urban studies.…”
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Bayesian optimization of hybrid quantum LSTM in a mixed model for precipitation forecasting by Yumin Dong, Huanxin Ding

“…Precipitation forecasting has important applications in meteorological research. Accurate forecasting is of great significance for reducing the impact of floods, optimizing crop planting plans, rationally allocating water resources, and ensuring traffic safety. …”
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Opinion mining using ensemble model for restaurant feedback analysis by Rohan S. Kamath, M. R. Kaushik, M. Ramakrishna

“…Abstract The research work introduces a new method for sentiment analysis in the context of restaurant evaluations, emphasizing the optimization of the trade-off between processing time and accuracy. …”
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Comparison of CNN Architectures for Pre-Cancerous Cervical Lesion Classification Based on Colposopy Images Using IARC and AnnoCerv Datasets by Sigit Prasetyo Noprianto, Siti Nurmaini, Dian Palupi Rini

“…Experiments were conducted using default hyperparameters: batch size of 32, learning rate of 0.001, and 100 epochs. …”
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Usage of Neural-Based Predictive Modeling and IIoT in Wind Energy Applications by Adrian-Nicolae Buturache, Stelian Stancu

“…At the time of this study, no prior research studies have presented a direct comparison between feedforward, recurrent, and convolutional neural networks ‒ these being the most important in the field of supervised learning.…”
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Deep Learning Approach in Seismology: Enhancing Earthquake Forecasting using K-Means Clustering and LSTM Networks by Tyanita Puti Marindah Wardhani, Zulkifli Tahir, Elly Warni, Anugrayani Bustamin, Muhammad Alief Fahdal Imran Oemar, Muhammad Alwi Kayyum

“…In addition, predicted seismic occurrences are plotted on a map to display their geographic location within the specified research region. This research provides significant value in facilitating the efficient distribution of resources, such as evacuating residents impacted by earthquakes or reinforcing buildings and infrastructure, for emergency responders and policymakers. …”
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An advanced CNN-attention model with IFTTA optimization for prediction air consumption of relay nozzles by Shen Min, Shao Ning, Cao Yongbo, Xiong Xiaoshuang, Yang Xuezheng, Wang Zhen, Yu Lianqing

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Edge-Fog Computing-Based Blockchain for Networked Microgrid Frequency Support by Ying-Yi Hong, Francisco I. Alano, Yih-der Lee, Chia-Yu Han

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Machine learning analysis of drug solubility via green approach to enhance drug solubility for poor soluble medications in continuous manufacturing by Ahmed A. Lahiq, Abdullah A. Alshehri, Shaker T. Alsharif

“…A GWO (Grey Wolf Optimization) method was also used to tune their hyperparameters. All three models have significant performances on estimation of CP solubility. …”
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Financial Market Evaluation Utilizing an Optimized Deep-Learning Model: A Case Study of the Nikkei 225 by Karthikeyan M P

“…The precision of the stock market forecasts can be improved using metaheuristic algorithms such as the Moth-flame optimizer, which will provide the best optimization of the hyperparameters for an LSTM model. The purpose of this research is to forecast stock prices by the MFO model with the LSTM network. …”
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Development of a Fault Prediction Algorithm for Marine Propulsion Energy Storage System by Jaehoon Lee, Sang-Kyun Park, Salim Abdullah Bazher, Daewon Seo

“…Bayesian optimization is applied to fine-tune hyperparameters, ensuring high predictive accuracy. Additionally, a recursive multi-step prediction model is developed to anticipate long-term battery performance trends. …”
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Modeling the determinants of attrition in a two-stage epilepsy prevalence survey in Nairobi using machine learning by Daniel M. Mwanga, Isaac C. Kipchirchir, George O. Muhua, Charles R. Newton, Damazo T. Kadengye, Abankwah Junior, Albert Akpalu, Arjune Sen, Bruno Mmbando, Charles R. Newton, Cynthia Sottie, Dan Bhwana, Daniel Mtai Mwanga, Damazo T. Kadengye, Daniel Nana Yaw, David McDaid, Dorcas Muli, Emmanuel Darkwa, Frederick Murunga Wekesah, Gershim Asiki, Gergana Manolova, Guillaume Pages, Helen Cross, Henrika Kimambo, Isolide S. Massawe, Josemir W. Sander, Mary Bitta, Mercy Atieno, Neerja Chowdhary, Patrick Adjei, Peter O. Otieno, Ryan Wagner, Richard Walker, Sabina Asiamah, Samuel Iddi, Simone Grassi, Sloan Mahone, Sonia Vallentin, Stella Waruingi, Symon Kariuki, Tarun Dua, Thomas Kwasa, Timothy Denison, Tony Godi, Vivian Mushi, William Matuja

“…Conclusion: These findings can aid researchers plan targeted mobilization for scheduled clinical appointments to improve follow-up rates. …”
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An optimized system for predicting energy usage in smart grids using temporal fusion transformer and Aquila optimizer by Namdeo Baban Badhe, Rahul P. Neve, Vijaykumar P. Yele, Swati Abhang, Komal Madhukar Dhule, Darshan Mali

“…This research presents an optimized system for predicting energy usage in smart grids by integrating the Temporal Fusion Transformer (TFT) with the Aquila Optimizer (AO). …”
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A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O² Facilities by Arnatchai Techaviseschai, Sansiri Tarnpradab, Vasco Chibante Barroso, Phond Phunchongharn

“…The ALICE (A Large Ion Collider Experiment) detector at the Large Hadron Collider (LHC), operated by the European Organization for Nuclear Research (CERN), is dedicated to heavy-ion collisions. …”
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Probabilistic analysis of active earth pressures in spatially variable soils using machine learning and confidence intervals by Tran Vu-Hoang, Tan Nguyen, Jim Shiau, Duy Ly-Khuong, Hung-Thinh Pham-Tran

“…By combining computational methods, machine learning, and uncertainty quantification, this research enhances geotechnical design practices, ensuring more reliable and cost-effective solutions.…”
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An Interpretable Hybrid Deep Learning Model for Molten Iron Temperature Prediction at the Iron-Steel Interface Based on Bi-LSTM and Transformer by Zhenzhong Shen, Weigang Han, Yanzhuo Hu, Ye Zhu, Jingjing Han

“…Most of the existing studies focus on the prediction of molten iron temperature in torpedo tanks, but there is a significant research gap in the prediction of molten iron ladle temperature drop, especially as the ladle is increasingly used to replace the torpedo tank in the transportation process, this research gap has not been fully addressed in the existing literature. …”
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Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification by Tales Boratto, Gabriel de Oliveira Costa, Alexsandro Meireles, Anna Klara Sá Teles Rocha Alves, Camila M. Saporetti, Matteo Bodini, Alexandre Cury, Leonardo Goliatt

“…However, the classification efficiency reported in such research works suggests the possibility of improvement. …”
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An explainable RoBERTa approach to analyzing panic and anxiety sentiment in oral health education YouTube comments by Pradeep Kumar Yadalam, Mohamed Thaha, Prabhu Manickam Natarajan, Carlos M. Ardila

“…This study uses RoBERTa, a state-of-the-art language model, to advance Natural Language Processing (NLP) research and enable real-time feedback in social media environments. …”
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