The Controlling Factors and Prediction of Deep-Water Mass Transport Deposits in the Pliocene Qiongdongnan Basin, South China Sea by Jiawang Ge, Xiaoming Zhao, Qi Fan, Weixin Pang, Chong Yue, Yueyao Chen

“…Our study indicates that a random forest artificial intelligence algorithm could be useful in predicting the susceptibility of deep-water MTDs and can be applied to other study areas to predict and avoid submarine disasters caused by wasting processes.…”
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Battery Health Prediction with Singular Spectrum Analysis and Grey Wolf Optimized Long Short-Term Memory Networks by Chengti Huang, Na Li, Jianqing Zhu, Shengming Shi

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A comparative study of hybrid adaptive neuro-fuzzy inference systems to predict the unconfined compressive strength of rocks by Wei Cao

“…Abstract The accurate prediction of unconfined compressive strength (UCS) in rock samples is critical for the successful planning, design, and implementation of mining and civil engineering projects. …”
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Modality-based Modeling with Data Balancing and Dimensionality Reduction for Early Stunting Detection by Yohanes Setiawan, Mohammad Hamim Zajuli Al Faroby, Mochamad Nizar Palefi Ma’ady, I Made Wisnu Adi Sanjaya, Cisa Valentino Cahya Ramadhani

“…The study achieves the best F1 scores of 0.96, 0.91, and 0.90 for tabular-only, image-only, and image-tabular modalities, respectively, demonstrating the effectiveness of data balancing and dimensionality reduction techniques.…”
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Optimization Study of Centrifugal Fan Volute Parameters based on Non-dominated Sorting Genetic Algorithm III Algorithm by J. L. Li, X. J. Wang, H. Gong, J. J. Wang

“…An efficient and accurate BP neural network was established as a surrogate model for predicting volute performance, and optimal design parameter combinations were obtained using the NSGA-III algorithm. …”
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<p><strong>Evaluation of geostatistical method and hybrid artificial neural network with imperialist competitive algorithm for predicting distribution pattern of <em>Tetranychus</em> <em>urticae</em> (Acari: Tetranychidae) in cucumber field of Behbahan, Iran</strong></p> by Alireza Shabaninejad, Bahram Tafaghodinia, Nooshin Zandi-Sohani

“…In Geostatistics methods ordinary kriging, and ANN with imperialist competitive algorithm were evaluated. Comparison of ANN and geostatistical showed that ANN capability is more than ordinary kriging method so that the ANN predicts distribution of this pest dispersion with 0.98 coefficient of determination and 0.0038 mean squares errors lower than the Geostatistical methods. …”
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Huffman coding-based data reduction and quadristego logic for secure image steganography by Irsyad Fikriansyah Ramadhan, Ntivuguruzwa Jean De La Croix, Tohari Ahmad, Andre Uzamurengera

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The Role of Immersive Experience in Anxiety Reduction: Evidence from Virtual Reality Sessions by Dominika Wilczyńska, Tamara Walczak-Kozłowska, David Alarcón, María José Arenilla, Jose Carlos Jaenes, Marcelina Hejła, Mariusz Lipowski, Joanna Nestorowicz, Henryk Olszewski

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Developing a machine learning algorithm to predict psychotropic drugs-induced weight gain and the effectiveness of anti-obesity drugs in patients with severe mental illness: Protocol for a prospective cohort study by Hye Jun Lee, Na Yeon Kim, Da Seul Kim, Youngbin Kim, Jung-Ha Kim, Doug Hyun Han, Sun Mi Kim

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Predicting specific wear rate of laser powder bed fusion AlSi10Mg parts at elevated temperatures using machine learning regression algorithm: Unveiling of microstructural morphology analysis by Vijaykumar S. Jatti, R. Murali Krishnan, A. Saiyathibrahim, V. Preethi, Suganya Priyadharshini G, Abhinav Kumar, Shubham Sharma, Saiful Islam, Dražan Kozak, Jasmina Lozanovic

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An external validation of the QCOVID3 risk prediction algorithm for risk of hospitalisation and death from COVID-19: An observational, prospective cohort study of 1.66m vaccinated adults in Wales, UK. by Jane Lyons, Vahé Nafilyan, Ashley Akbari, Stuart Bedston, Ewen Harrison, Andrew Hayward, Julia Hippisley-Cox, Frank Kee, Kamlesh Khunti, Shamim Rahman, Aziz Sheikh, Fatemeh Torabi, Ronan A Lyons

“…<h4>Introduction</h4>At the start of the COVID-19 pandemic there was an urgent need to identify individuals at highest risk of severe outcomes, such as hospitalisation and death following infection. The QCOVID risk prediction algorithms emerged as key tools in facilitating this which were further developed during the second wave of the COVID-19 pandemic to identify groups of people at highest risk of severe COVID-19 related outcomes following one or two doses of vaccine.…”
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A deep-learning algorithm using real-time collected intraoperative vital sign signals for predicting acute kidney injury after major non-cardiac surgeries: A modelling study. by Sehoon Park, Soomin Chung, Yisak Kim, Sun-Ah Yang, Soie Kwon, Jeong Min Cho, Min Jae Lee, Eunbyeol Cho, Jiwon Ryu, Sejoong Kim, Jeonghwan Lee, Hyung Jin Yoon, Edward Choi, Kwangsoo Kim, Hajeong Lee

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Enhancing Upper Limb Exoskeletons Using Sensor-Based Deep Learning Torque Prediction and PID Control by Farshad Shakeriaski, Masoud Mohammadian

Subjects: “…torque estimation and prediction…”
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Exploring Evolutionary Algorithms for Optimal Power Flow: A Comprehensive Review and Analysis by Harish Pulluri, Vedik Basetti, B. Srikanth Goud, CH. Naga Sai Kalyan

“…Multi-objective approaches are frequently employed in modern OPF problems to balance trade-offs between competing objectives like cost minimization, loss reduction, and environmental impact. This article presents an in-depth review of various OPF problems and the wide array of algorithms, both traditional and evolutionary, applied to solve these problems, paying special attention to wind power integration and multi-objective optimization strategies.…”
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Making predictions for overhead power lines applying predictive analytics by Yu. N. Kondrashova, A. M. Tretyakovͬ, A. V. Shalimov

“…It should be noted that the general trend is a high percentage of wear and tear, moral and physical aging of the main and auxiliary electrical equipment.Goal - the application of methods of mathematical analysis and predictive analytics to predict the number of failures of electrical equipment and the development of an algorithm for calculating forecasting to assess damages in the future.Materials and methods. …”
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Continuous prediction of knee joint angle in lower limbs based on sEMG: a method combining an improved ZOA optimizer and attention-enhanced GRU by Jian Lv, Binhao Huang, Ligang Qiang

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Risk prediction of QTc prolongation occurrence in cancer patients treated with commonly used oral tyrosine kinase inhibitors: machine learning modeling or conventional statistical... by Hsiang-Wen Lin, Tien-Chao Lin, Chien-Ning Hsu, Tzu-Pei Yeh, Yu-Chieh Chen, Liang-Chih Liu, Chen-Yuan Lin

“…The backward LR method and seven ML algorithms were applied to train and test the prediction models. …”
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Metallic Artifacts’ Reduction in Microtomography Using the Bone- and Soft-Tissue Decomposition Method by Jan Juszczyk, Jakub Pałachniak, Ewa Piętka

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ANN-based pulse shape discrimination for background reduction in neutron depth profiling by Hwijoon Jeong, Jinhwan Kim, Byung Gun Park, Kyung Taek Lim

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An Optimization Mechanism Intended for Static Power Reduction Using Dual-𝑉thTechnique by Rodolfo P. Santos, Gabriela S. Clemente, Abel Silva-Filho, Cristiano Araújo, Adriano Sarmento, Manoel Lima, Edna Barros

“…The fact that only some cells are replaced every iteration results in a reduction of the runtime of the algorithm. Additionally, results showed a reduction in static power up to 39.28%, when applying the proposed approach in the ISCAS85 benchmark circuits.…”
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