Blending physical and artificial intelligence models to improve satellite-derived bathymetry mapping by Daniel García-Díaz, Sandra Paola Viaña-Borja, Mar Roca, Gabriel Navarro, Isabel Caballero

“…In this study, we evaluated the performance of two different algorithms for estimating SDB in two areas of the Western Mediterranean: a physics-driven model and an Artificial Neural Network (ANN). …”
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Educational improvement through machine learning: Strategic models for better PISA scores. by Bilal Baris Alkan, Serafettin Kuzucuk, Şevki Yetkin Odabasi, Leyla Karakuş

“…The study found that the main factors influencing the success of students in countries that perform well in the PISA exam are essentially access to information technology, weekly hours of instruction in the subject, economic-social and cultural status, parents' occupation, level of metacognition, awareness of PISA, sense of competition and attitudes towards reading. New prediction models based on these variables were proposed. The proposed models will give a significant advantage to policy makers who want to improve their country's PISA score and implement appropriate education policies.…”
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Risk assessment and hybrid algorithm transportation path optimization model for road transport of dangerous goods by Qiankun Jiang, Haiyan Wang

“…After experimental verification, the improved hybrid algorithm has optimized the path transportation time by 13.9 % compared to a single algorithm model. …”
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Improving the efficiency of frequency regulation for High-Power diesel generators by Shchasiana Arhun, Aleksandr Bogajevskiy, Andrii Hnatov, Nadezhda Kunicina

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An alternative method to improve gravity field models by incorporating GOCE gradient data by Xiaoyun Wan, Jiangjun Ran

“…The aim of this paper is to present an alternative method that can be used to improve existing gravity field models via the application of gradient data from Gravity field and Ocean Circulation Explorer (GOCE). …”
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ALGORITHMIC AND PROGRAM IMPLEMENTATION OF THE PLAGIARISM DEFINITION IN LEARNING MANAGEMENT SYSTEMS by Y. B. Popova, A. V. Goloburda

“…The authors suggest a modification of the vector model to improve the accuracy of determining similar documents by creating an N-list of each document separately. …”
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Model-Free Predictive Control Based on RLS Algorithm for Grid-Forming Inverters With Virtual Synchronous Generator by Min Huang, Huiying Zhang, Kangan Wang, Zhilei Yao, Weimin Wu

“…The implementation of MFPC improves the parameter robustness of the system. Building on this, the improvement schemes of algorithmic parameter initialization, delay compensation and current limiting capability are further investigated. …”
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Biomechanical Modeling of Selected Methods of Load Carriage to Improve Military Capabilities of Troops by Mostafa Hajlotfalian, Alireza Redaei, Heydar Sadeghi

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Improving parking availability prediction in smart cities with IoT and ensemble-based model by Stéphane Cédric Koumetio Tekouabou, El Arbi Abdellaoui Alaoui, Walid Cherif, Hassan Silkan

“…The tests that we carried out on the Birmingham parking data set allowed to reach a Mean Absolute Error (MAE) of 0.06% on average with the algorithm of Bagging Regression (BR). This results have thus improved the best existing performance by over 6.6% while dramatically reducing system complexity.…”
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Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm by Juncheng Fu, Zhengxiang Song, Jinhao Meng, Chunling Wu

“…Addressing the non-linear and non-stationary characteristics of battery capacity sequences, a novel method for predicting lithium battery SOH is proposed using a deep hybrid kernel extreme learning machine (DHKELM) optimized by the improved black-winged kite algorithm (IBKA). First, to address the limitations of traditional extreme learning machines (ELMs) in capturing non-linear features and their poor generalization ability, the concepts of auto encoders (AEs) and hybrid kernel functions are introduced to enhance ELM, resulting in the establishment of the DHKELM model for SOH prediction. …”
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Log-Viterbi algorithm applied on second-order hidden Markov model for human activity recognition by Yang Sung-Hyun, Keshav Thapa, M Humayun Kabir, Lee Hee-Chan

“…Therefore, this approach maximizes the probability of activity recognition with improved accuracy and reduced time complexity. We compared our proposed algorithm among other famous probabilistic models such as Naïve Bayes, condition random field, hidden Markov model, and hidden semi-Markov model using three datasets in the smart home environment. …”
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Hybridization of Machine Learning Algorithms and an Empirical Regression Model for Predicting Debris-Flow-Endangered Areas by Xiang Wang, Mi Tian, Qiang Qin, Jingwei Liang

“…Results show that compared with the three individual machine-learning models, hybridization of machine-learning algorithms and the empirical model results in R2, RMSE, and MAE improved by 70.5%, 32.9%, and 41.1%, respectively. …”
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Postpartum depression risk prediction using explainable machine learning algorithms by Xudong Huang, Lifeng Zhang, Chenyang Zhang, Jing Li, Chenyang Li

“…Feature selection was performed using LASSO regression and the Boruta algorithm. Eight machine learning algorithms were then employed to construct the prediction models. …”
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Classification of 3D CAD Models considering the Knowledge Recognition Algorithm of Convolutional Neural Network by Weiwei Wang, Dandan Sun

“…In order to improve the classification effect of the 3D CAD model, this paper combines the knowledge recognition algorithm of convolutional neural network to construct the 3D CAD model classification model. …”
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Optimized Grasshopper Optimisation Algorithm enabled DETR (DEtection TRansformer) model for skin disease classification. by Shakti Kundu, Yogesh Kumar Sharma, Khan Vajid Nabilal, Gopalsamy Venkatesan Samkumar, Sultan Mesfer Aldossary, Shanu Kuttan Rakesh, Nasratullah Nuristani, Arshad Hashmi

“…The novelty of this study lies in integrating the Grasshopper Optimisation Algorithm (GOA) with a DETR (DEtection TRansformer) model which is developed for the classification of skin disease. …”
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Comment on “Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling” by Jasper A. Vrugt

“…Abstract Huang and Merwade (2023), https://doi.org/10.1029/2023wr034947, hereafter conveniently referred to as HM23, wrongly claim improvement of their method for postprocessing multi‐model water stage predictions using Bayesian Model Averaging (BMA). …”
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DA-YOLOv7: A Deep Learning-Driven High-Performance Underwater Sonar Image Target Recognition Model by Zhe Chen, Guohao Xie, Xiaofang Deng, Jie Peng, Hongbing Qiu

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Predictive modeling of adolescent suicidal behavior using machine learning: Key features and algorithmic insights by Priya Metri, Swetta Kukreja

“…This review highlights existing gaps in cross-cultural generalization and calls for the development of interpretable and hybrid models for improved risk prediction.This review aims to conduct a comprehensive examination of the etiological factors contributing to the development of suicidal thoughts in students, with the goal of enabling early detection through the application of AI and machine learning techniques.This paper aims to review the current state-of-the-art, highlight the limitations, and emphasizes the need to shift toward hybrid and ensemble deep learning models, which have shown early promise but lack extensive analysis in current literature.…”
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A prediction model for soil heavy metal content based on improved tensor completion by Zhangang Wang, Wenjie Li, Tianhe Yun, Jiaxiang Qi

“…This paper presents a novel method for predicting soil heavy metal content using an advanced tensor completion algorithm. The proposed method estimates heavy metal concentrations at unsampled locations by constructing a prediction model within the Coarse-to-Fine (C2F) framework, leveraging data from sampled points. …”
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Advanced predictive disease modeling in biomedical IoT using the temporal adaptive neural evolutionary algorithm by Chandragandhi S, Arvind C, Srihari K

“…TANEA leverages temporal data patterns, adapts to dynamic changes in sensor readings, and optimizes feature selection through an evolutionary mechanism, resulting in a more precise and reliable predictive model. Experimental evaluations demonstrate TANEA’s superior performance over traditional methods, achieving improved accuracy, reduced computational overhead, and faster convergence rates. …”
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