Optimization of Thermal and Pressure Drop Performance in Circular Pin Fin Heat Sinks Using the TOPSIS Method by Jemit Adhyaru, Mohan Uma, Vedagiri Praveena, Prabhu Sethuramalingam

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Data-driven assessment of corrosion in reinforced concrete structures embedded in clay dominated soils by Shahbaz Ahmad, Siraj Ahmad, Sabih Akhtar, Faraz Ahmad, Mujib Ahmad Ansari

“…Abstract The integration of Artificial Intelligence techniques, particularly Artificial Neural Networks (ANNs), has transformed predictive modeling in structural and durability engineering. …”
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A Geoanalytical and AI-Driven Approach for Optimising Biomass Energy Generation from Key Economic Crops in Northeastern Thailand by Konyai Supasit, Khuanmar Kulyakorn, Kanarkard Wanida

“…Furthermore, artificial intelligence (AI) technologies were employed to forecast key sugarcane parameters, including cane yield, cane cultivation area, and production output. …”
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Machine Learning Techniques and Recommender Systems for Large Educational Data by Ammar Mohammed, Murtadha Hamad

“…This study proposed machine learning (ML) models such as K Nearest Neighbor (KNN) and Singular Value Decomposition (SVD) while this paper utilizing evaluations of performance metrics like root mean square error (RMSE), mean absolute error (MAE), hit rate (HR), average reciprocal hit rating (ARHR), accuracy, and recall. …”
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A Double-Layer LSTM Model Based on Driving Style and Adaptive Grid for Intention-Trajectory Prediction by Yikun Fan, Wei Zhang, Wenting Zhang, Dejin Zhang, Li He

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Cyclic dual latent discovery for improved blood glucose prediction through patient–provider interaction modeling: a prediction study by Suyeon Park, Seoyoung Kim, Dohyoung Rim

“…Results CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. …”
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Cobb angle prediction for adolescent idiopathic scoliosis via an explainable machine learning model by Yu Ding, Bin Li, Xiaoyong Guo

“…Cross-validation estimation shows a satisfactory performance for predicting future spine curvature (Cobb angle). The root mean square error (∘), the mean absolute percentage error (∘), and the Pearson correlation coefficient are 3.69 ± 1.23, 2.81 ± 1.69, and 0.92 ± 0.01, respectively. …”
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Improving Deep Learning for Forecasting Accuracy in Financial Data by Shih-Lin Lin, Hua-Wei Huang

“…However, the complex nature of financial data makes it difficult to get accurate forecasts. Artificial intelligence, which has been shown to be suitable for analyzing very complex problems, can be applied to financial forecasting. …”
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Heterogeneous AI Music Generation Technology Integrating Fine-Grained Control by Hongtao Wang, Li Gong

“…As artificial intelligence algorithms continue to advance, researchers have increasingly harnessed their capabilities to generate music that resonates with human emotions, offering a novel means of alleviating the escalating pressures of contemporary life. …”
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Indirect Determination Approach of Blast-Induced Ground Vibration Based on a Hybrid SSA-Optimized GP-Based Technique by Zhaoxin Jiang, Hongyan Xu, Hui Chen, Bei Gao, Shijie Jia, Zhi Yu, Jian Zhou

“…For evaluating the peak particle velocity resulting from the quarry blast, a robust artificial intelligence system combined with the salp swarm algorithm (SSA) and Gaussian process (GP) was proposed, and the SSA was used to find the optimal hyperparameters of the GP here. …”
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Explainable AI and optimized solar power generation forecasting model based on environmental conditions. by Rizk M Rizk-Allah, Lobna M Abouelmagd, Ashraf Darwish, Vaclav Snasel, Aboul Ella Hassanien

“…This paper proposes a model called X-LSTM-EO, which integrates explainable artificial intelligence (XAI), long short-term memory (LSTM), and equilibrium optimizer (EO) to reliably forecast solar power generation. …”
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Compressive Strength Prediction Using Coupled Deep Learning Model with Extreme Gradient Boosting Algorithm: Environmentally Friendly Concrete Incorporating Recycled Aggregate by Mayadah W. Falah, Sadaam Hadee Hussein, Mohammed Ayad Saad, Zainab Hasan Ali, Tan Huy Tran, Rania M. Ghoniem, Ahmed A. Ewees

“…The developed approach is compared to the well-known artificial intelligence (AI) approaches named multivariate adaptive regression spline (MARS), extreme learning machines (ELMs), and random forests (RFs). …”
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Indoor Air Quality Prediction in Sick Building Using Machine and Deep Learning: Comparative Analysis by Hayder Qasim Flayyih, Jumana Waleed, Amer M. Ibrahim

“…This analysis aims to implement the specified models and assess the efficiency of their prediction by computing several performance metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Median Absolute Error (Median AE), and Coefficient of determination (R2). …”
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Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction by Mahmood Shahabi, Mohammad Ali Ghorbani, Sujay Raghavendra Naganna, Sungwon Kim, Sinan Jasim Hadi, Samed Inyurt, Aitazaz Ahsan Farooque, Zaher Mundher Yaseen

“…The proposed MM-GANN model is a reliable intelligence-based approach for the assessment of soil quality parameters intended for sustainability and management prospects.…”
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Research on the Liquor Yield Prediction Model Integrating Hybrid Kernel Support Vector Regression and Dung Beetle Optimizer by Qinwen Deng, Yibo Xu, Qiang Han, Suyi Zhang, Xianguo Tuo

“…The swarm intelligence optimization model exhibits smaller prediction errors than traditional machine learning methods. …”
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Lingual nerve injuries: recognized complications or preventable errors? by Frederic Van der Cruyssen, Frederic Van der Cruyssen, Frederic Van der Cruyssen, Robin Willaert, Robin Willaert, Michael Miloro

“…These may occur during third molar extractions, inferior alveolar nerve blocks, implant placement, root canal treatment and other dental, oral, and maxillofacial procedures. …”
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Predicting the uplift capacity of circular anchors in frictional-cohesive soils using Kolmogorov-Arnold networks by Tran Vu-Hoang, Tan Nguyen, Jim Shiau, Hung-Thinh Pham-Tran, Trung Nguyen-Thoi

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Multi-station water level forecasting using advanced graph convolutional networks with adversarial learning by Xinhai Han, Xiaohui Li, Jingsong Yang, Jiuke Wang, Guoqi Han, Jun Ding, Hui Shen, Jun Yan, Dake Chen

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Filling the data gap between GRACE and GRACE-FO based on a two-step reconstruction method by Fengmin Hu, Beibei Yang, Zushuai Wei, Changlu Cui, Lingkui Meng

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Predicting hydrocarbon reservoir quality in deepwater sedimentary systems using sequential deep learning techniques by Xiao Hu, Jun Xie, Xiwei Li, Junzheng Han, Zhengquan Zhao, Hamzeh Ghorbani

“…The Adam-LSTM model outperformed the others, achieving a Root Mean Square Error of 0.009 and a correlation coefficient (R2) of 0.9995, indicating excellent predictive performance. …”
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