Towards the implementation of automated scoring in international large-scale assessments: Scalability and quality control by Ji Yoon Jung, Lillian Tyack, Matthias von Davier

“…The results showed that the supervised learning approach, particularly combining multiple machine translations with artificial neural networks (MMT_ANNs), showed comparable performance to human scoring. …”
Get full text

Real-Time Diagnostic Technique for AI-Enabled System by Hiroaki Itsuji, Takumi Uezono, Tadanobu Toba, Subrata Kumar Kundu

“…The last few decades have witnessed a dramatic evolution of Artificial Intelligence (AI) algorithms, represented by Deep Neural Networks (DNNs), resulting in AI-enabled systems being significantly dominant in various fields, including robotics, healthcare, and mobility. …”
Get full text

Evaluation of Three Satellite Precipitation Products TRMM 3B42, CMORPH, and PERSIANN over a Subtropical Watershed in China by Junzhi Liu, Zheng Duan, Jingchao Jiang, A-Xing Zhu

“…This study conducted a comprehensive evaluation of three satellite precipitation products (TRMM (Tropical Rainfall Measuring Mission) 3B42, CMORPH (the Climate Prediction Center (CPC) Morphing algorithm), and PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks)) using data from 52 rain gauge stations over the Meichuan watershed, which is a representative watershed of the Poyang Lake Basin in China. …”
Get full text

A Fast-Convergent Hyperbolic Tangent PSO Algorithm for UAVs Path Planning by Muhammad Haris, Dost Muhammad Saqib Bhatti, Haewoon Nam

“…This innovation draws inspiration from the activation functions employed in neural networks, with the singular aim of accelerating convergence. …”
Get full text

Intelligent IoT-Based Network Clustering and Camera Distribution Algorithm Using Reinforcement Learning by Islam T. Almalkawi, Rami Halloush, Mohammad F. Al-Hammouri, Alaa Alghazo, Loiy Al-Abed, Mohammad Amra, Ayooub Alsarhan, Sami Aziz Alshammari

“…Thus, we propose a smart and efficient camera distribution system based on machine learning using two Reinforcement Learning (RL) methods: Q-Learning and neural networks. Our proposed approach initially uses a geometric distributed network clustering algorithm that optimizes camera placement based on the camera Field of View (FoV). …”
Get full text

Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features by Ayodeji O. J. Ibitoye, Joseph D. Akinyemi, Olufade F. W. Onifade

“…These top-15 feature pairs were fed into five different ML models (decision tree (DT), neural networks (NN), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGB)) for predicting the likelihood of diabetes, while also feeding the direct features (without correlated pairing) separately into the same 5[Formula: see text]ML models. …”
Get full text

A comparative study of machine learning algorithms for fall detection in technology-based healthcare system: Analyzing SVM, KNN, decision tree, random forest, LSTM, and CNN by Afuan Lasmedi, Isnanto R. Rizal

“…This study aims to compare the performance of six classification algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN) in detecting fall incidents using wearable sensor data such as accelerometers and gyroscopes. …”
Get full text

Explainable machine learning framework for cataracts recognition using visual features by Xiao Wu, Lingxi Hu, Zunjie Xiao, Xiaoqing Zhang, Risa Higashita, Jiang Liu

“…Abstract Cataract is the leading ocular disease of blindness and visual impairment globally. Deep neural networks (DNNs) have achieved promising cataracts recognition performance based on anterior segment optical coherence tomography (AS-OCT) images; however, they have poor explanations, limiting their clinical applications. …”
Get full text

Oscillatory Corticospinal Activity during Static Contraction of Ankle Muscles Is Reduced in Healthy Old versus Young Adults by Meaghan Elizabeth Spedden, Jens Bo Nielsen, Svend Sparre Geertsen

“…Aging is accompanied by impaired motor function, but age-related changes in neural networks responsible for generating movement are not well understood. …”
Get full text

Fear extinction retention in children, adolescents, and adults by Ebba Widegren, Johan Vegelius, Matilda A. Frick, Ashika A. Roy, Stefan Möller, Johan Lundin Kleberg, Johanna Motilla Hoppe, Olof Hjorth, David Fällmar, Daniel S. Pine, Karin Brocki, Malin Gingnell, Andreas Frick

“…In contrast to findings in rodents, fear conditioning in humans may elicit similar physiological responses and recruit similar neural networks from childhood to adulthood.…”
Get full text

A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges by Zongxu Liu, Hui Guo, Yingshuai Zhang, Zongliang Zuo

“…Machine learning methods, especially deep learning approaches such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ensemble learning techniques like XGBoost, excel in addressing the nonlinearity and complexity of wind power data. …”
Get full text

Mechanisms Underlying Adaptation of Respiratory Network Activity to Modulatory Stimuli in the Mouse Embryo by Marc Chevalier, Rafaël De Sa, Laura Cardoit, Muriel Thoby-Brisson

“…Respiratory rhythmogenesis is controlled by neural networks located in the brainstem. One area considered to be essential for generating the inspiratory phase of the respiratory rhythm is the preBötzinger complex (preBötC). …”
Get full text

Effect of physical activity on structural asymmetry of mouse hippocampus by T. A. Zapara, A. V. Romashchenko, A. L. Proskura, A. S. Ratushnyak

“…The reconstruction and analysis of protein­protein interactions that ensure the survival of a large number of new neurons and their integration into existing neural networks in the hippocampus have been carried out. …”
Get full text

Motor control method using single-sensor phase current reconstruction by Yin Lu, Yuntian Huang, Hao Guo

“…Simultaneously, optimization algorithms like neural networks are employed to learn from historical data to predict and estimate the current values of the three phases. …”
Get full text

A scientometric review of the relationship between learning agility and work engagement in modern management context by Farira Nareswari, Rini Juni Astuti

“…Machine learning, artificial neural networks, and predictive analytics can improve learning agility and work engagement. …”
Get full text

Feature fusion-based collaborative learning for knowledge distillation by Yiting Li, Liyuan Sun, Jianping Gou, Lan Du, Weihua Ou

“…Deep neural networks have achieved a great success in a variety of applications, such as self-driving cars and intelligent robotics. …”
Get full text

Three-dimensional design, simulation and optimization of a centrifugal compressor impeller with double-splitter blades by Mohammadjavad Tasharrofi, Mojtaba Heidarian Shahri, Ali Madadi

“…Since the optimization process only using genetic algorithms is very time-consuming and has high computational costs, artificial neural networks were used to reduce costs. The objective function in this optimization process was to increase efficiency while maintaining the flow rate and pressure ratio at the design point. …”
Get full text

A Hybrid FEM-CNN for Image-Based Severity Prediction of Corroded Offshore Pipelines by Mohammad Fadzil Najwa, Muda Mohd Fakri, Abdul Shahid Muhammad Daniel, Aziz Norheliena, Mohd Mohd Hairil, Mohd Amin Norliyati, Mohd Hashim Mohd Hisbany

“…The combination of the Finite Element Method (FEM) with Convolutional Neural Networks (CNNs) presents a key breakthrough in the assessment of the structural integrity of offshore pipelines. …”
Get full text

Lithium-Ion Battery State of Health Degradation Prediction Using Deep Learning Approaches by Talal Alharbi, Muhammad Umair, Abdulelah Alharbi

“…Three deep learning architectures 1D Convolutional Neural Networks (CNN), CNN plus Long Short-Term Memory (LSTM), and CNN plus Gated Recurrent Units (GRU) are used in the centralized approach. …”
Get full text

Chemical Process Fault Diagnosis Based on Improved ResNet Fusing CBAM and SPP by Xiaochen Yan, Yang Zhang, Qibing Jin

“…Firstly, 1D convolution is introduced in the construction of the model to reduce the number of parameters and training time, and shortcut connections are used to alleviate the network degradation problem of traditional deep neural networks. Second, a residual-CBAM module is proposed by combining residual networks with Convolutional Block Attention Module (CBAM). …”
Get full text