<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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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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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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From Social to Academic: Associations and Predictions Between Different Types of Peer Relationships and Academic Performance Among College Students by Jiadong Tian, Jiali Lin, Dagang Li

“…Subsequently, we used Random Forests and Neural Networks as baseline methods, and introduced Graph Convolutional Network and Dynamic Graph Convolutional Network algorithms, on top of a graph network model based on social characteristics, to predict students’ academic performances. …”
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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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Integrating user demographic parameters for mouse behavioral biometric-based assessment fraud detection in online education platforms by Aditya Subash, Insu Song, Ickjai Lee, Kyungmi Lee

Subjects: “…Cross-referencing fusion algorithm…”
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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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MODELLING OF PROCESS OF PUBLIC AND PROFESSIONAL EXAMINATION OF SUCCESS OF SOCIALIZATION OF CHILDREN IN SYSTEM OF ADDITIONAL EDUCATION by Anatoliy Babich, Radmila Ter-Grigor'yants

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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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Reduction of the Reconstruction Error With Lower and Upper Bounds in Synthetic Aperture Imaging Radiometers by Xiaocheng Yang, Zhenyi Yang, Jingye Yan, Lin Wu, Mingfeng Jiang, Wentao Lyu

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A Novel Efficient Feature Dimensionality Reduction Method and Its Application in Engineering by Zhun Cheng, Zhixiong Lu

“…To render feature dimensionality reduction more effective and faster, this paper proposes a new feature dimensionality reduction approach combining a sampling survey method with a heuristic intelligent optimization algorithm. …”
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openTSNE: A Modular Python Library for t-SNE Dimensionality Reduction and Embedding by Pavlin G. Poličar, Martin Stražar, Blaž Zupan

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MOMOS: The Multi-Object MKID Optical Spectrometer Simulator and Data Reduction Package by Crystal S. Kim, John I. Bailey III, Ronald A. López, W. Hawkins Clay, Benjamin A. Mazin

“…Treating simulator products as inputs, an algorithm was developed and implemented in the MOMOS data reduction package to calibrate and extract spectra.…”
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Feedforward Control of a Light-Weight Device Casing for Active Noise Reduction by Stanislaw WRONA, Marek PAWELCZYK

“…An adaptive control strategy based on the Least Mean Square (LMS) algorithm is used to update control filter parameters. …”
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