Prediction of speed of sound of deep eutectic solvents using artificial neural network coupled with group contribution approach by Ayat Hussein Adhab, Morug Salih Mahdi, Hardik Doshi, Anupam Yadav, R. Manjunatha, Sushil Kumar, Debasish Shit, Gargi Sangwan, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd

“…Since the ideal gas heat capacity of DESs is often unavailable, a machine learning (ML) approach, using artificial neural networks (ANNs) coupled with a Group Contribution (GC) method, is a promising technique. …”
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Clinical value of sublingual microcirculatory dysfunction for screening for primary aldosteronism in hypertensive patients by Wuhao Wang, Wei Liu, Yi Tang, Fang Sun, Hongbo He, Zhencheng Yan, Qiang Li, Zhiming Zhu, Zhiming Zhu

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Simulation of Net Ecosystem Carbon Flux in Rice Planting Area of Yangtze River Delta based on Multi-layer Perceptron Model by Wenyang XI, Jianjun HE, Zhilin WANG, Lifeng GUO, Yarong LI

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Assessing feature importance for forecasting soil moisture in subarctic regions using gridded historical and forecasted climate data by Mojtaba Saboori, Kedar Surendranath Ghag, Anandharuban Panchanathan, Epari Ritesh Patro, Ali Torabi Haghighi

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Nomogram combining dual-energy computed tomography features and radiomics for differentiating parotid warthin tumor from pleomorphic adenoma: a retrospective study by Zhiwei Gong, Jianying Li, Yilin Han, Shiyu Chen, Lijun Wang

“…This study aimed to evaluate a nomogram combining dual-energy computed tomography (DECT) quantitative parameters and radiomics to enhance diagnostic precision.MethodsThis retrospective study included 120 patients with pathologically confirmed PA or WT, randomly divided into training and test sets (7:3). …”
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A mobile hybrid deep learning approach for classifying 3D-like representations of Amazonian lizards by Arthur Gonsales da Silva, Arthur Gonsales da Silva, Roger Pinho de Oliveira, Caio de Oliveira Bastos, Caio de Oliveira Bastos, Elena Almeida de Carvalho, Bruno Duarte Gomes

“…In this study, we present the development of a hybrid machine learning-based tool suitable for deployment on mobile devices. …”
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Artificial Intelligence for Predicting Delivery Modes: A Systematic Review of Applications, Challenges, and Future Directions by Yu Zhou, Jing Li, Xiaomei Hou, Zhen Li, Yanan Xu, Yan Wang, Mingze Sun, Fumin Zheng, Enhui Guo, Jun Zhou

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Self SOC Estimation for Second-Life Lithium-Ion Batteries by Joelton Deonei Gotz, Emilson Ribeiro Viana, Jose Rodolfo Galvao, Fernanda Cristina Correa, Milton Borsato, Alceu Andre Badin

“…This work presents a system consisting of two Machine Learning (ML) layers to automatically estimate the state of charge (SOC) of SLB independent of the battery’s capacity or age. …”
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MRI Delta Radiomics to Track Early Changes in Tumor Following Radiation: Application in Glioblastoma Mouse Model by Mohammed S. Alshuhri, Haitham F. Al-Mubarak, Abdulrahman Qaisi, Ahmad A. Alhulail, Abdullah G. M. AlMansour, Yahia Madkhali, Sahal Alotaibi, Manal Aljuhani, Othman I. Alomair, A. Almudayni, F. Alablani

“…Delta radiomics features exhibited distinct patterns across different time points in the IR group, enabling machine learning models to achieve a high accuracy. …”
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Artificial Intelligent Model to Enhance Thermal Conductivity of TiO₂-Al₂O₃/Water-Ethylene Glycol-Based Hybrid Nanofluid for Automotive Radiator by Md. Munirul Hasan, Md. Mustafizur Rahman, Md. Arafatur Rahman, Suraya Abu Bakar, Mohammad Saiful Islam, Tarek Khalifa

“…ANN model was constructed using input parameters such as volume concentration and temperature, with the output being the conductivity. …”
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Diagnostic accuracy of artificial intelligence for the screening of prostate cancer in biparametric magnetic resonance imaging: a systematic review by Oksana V. Kryuchkova, Elena V. Schepkina, Natalia A. Rubtsova, Boris Y. Alekseev, Anton I. Kuznetsov, Svetlana V. Epifanova, Elena V. Zarya, Ali E. Talyshinskii

“…Moreover, 43% and 33% of the studies were dedicated to transition zone and prostate peripheral zone neoplasms, respectively, and 52% of the authors examined the whole prostate gland, without dividing it into zones. The most common machine-learning algorithms applied by the investigators were as follows: multiple logistic regression (76%), support vector machine (38%), and random forest (24%). …”
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Predicting age at first calving of dairy breed calves using whale optimization-based ensemble learning framework by Tewodros Shekure, Hussien Seid Worku, Sudhir Kumar Mohapatra, Tapan Kumar Das

“…This problem can be lessened by selecting best breed and modern animal breeding facilities, which require technologies like big data analysis and machine learning. In this study, a prediction model that can predict age at first calving of weaned calves based on their pre-weaning and weaning parameters, including dam’s parity number, season of calving, birth weight, pre-weaning health status, pre-weaning average daily weight gain (ADG), weaning age and weaning weight is developed. …”
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CONTROL RESEARCH AND AUTOMATION OF STOCHASTIC DEVIATIONS IN ORGANISATIONAL MANAGEMENT PRODUCTION PROCESS SYSTEMS by R. B. Aghajanyan, D. O. Baizhanova, M. V. Markosyan

“…Based on the proposed method of machine detection and identification of deviations, the information management system of CAPA procedures has been developed and successfully implemented at several enterprises within the pharmaceutical industry. …”
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SPECIFICITY AND TRENDS IN IMPROVEMENT OF TRACTOR TRAIN BRAKING DYNAMICS by G. A. Tayanovsky, G. A. Basalay

“…They represent clear design and operational parameters of the active tractor train. Such approach has made it possible to realize them in the form of a software application which is convenient for analysis of the braking process pertaining to the investigated objects in order to select means for improvement of braking dynamics, rational parameters of multi-path wheel drive and tire completing of the active tractor train under design. …”
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Working Element of a Horizontal Conveyor Type for Grass Raking by P. A. Eremin, V. V. Mikheev, A. Kh. Tekushev, M. Yu. Kostenko, V. P. Eremin

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Combining SVM and Naive Bayes Models using a Soft Voting Approach for Sentiment Analysis of Tong Tji Tea House by Fendi Pradana Saputra, Ozzi Suria

“…This study aims to analyze sentiment in Indonesian-language review texts using three machine learning models: Support Vector Machine (SVM), Naive Bayes (NB), and a combination of both through an Ensemble Soft Voting Classifier approach. …”
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A Digital Twin Framework With Bayesian Optimization and Deep Learning for Semiconductor Process Control by Chin-Yi Lin, Tzu-Liang Tseng, Tsung-Han Tsai

“…This paper introduces an intelligent optimization framework that integrates Digital Twin (DT) technology, deep learning, and a tailored Multi-Restart Bayesian Optimization with Random Initialization (MRBORI) to enhance parameter control and yield in semiconductor manufacturing. …”
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An adaptive hierarchical hybrid kernel ELM optimized by aquila optimizer algorithm for bearing fault diagnosis by Hao Yan, Liangliang Shang, Wan Chen, Mengyao Jiang, Tianqi lu, Fei Li

“…This paper proposes an intelligent bearing fault diagnosis method that improves classification accuracy using a stacked denoising autoencoder (SDAE) and adaptive hierarchical hybrid kernel extreme learning machine (AHHKELM). First, a hybrid kernel extreme learning machine (HKELM) is initially constructed, leveraging SDAE’s deep network architecture for automatic feature extraction. …”
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Improved Distributed Backdoor Attacks in Federated Learning by Density-Adaptive Data Poisoning and Projection-Based Gradient Updating by Jian Wang, Hong Shen, Wei Ke, Xue Hua Liu

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Online learning to accelerate nonlinear PDE solvers: Applied to multiphase porous media flow by Vinicius L.S. Silva, Pablo Salinas, Claire E. Heaney, Matthew D. Jackson, Christopher C. Pain

“…The proposed method rely on four pillars: (i) dimensionless numbers as input parameters for the machine learning model, (ii) simplified numerical model (two-dimensional) for the offline training, (iii) dynamic control of a nonlinear solver tuning parameter (numerical relaxation), (iv) and online learning for real-time improvement of the machine learning model. …”
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